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Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learning-based denoising methods are built on convolutional neural networks (CNNs), which concentrate on local information and have little capacity for multiple structures modeling. Transformer structures are capable of computing each pixel's response on a global scale, but their extensive computation requirements prevent them from being widely used in medical image processing. To reduce the impact of LDCT scans on patients, this paper aims to develop an image post-processing method by combining CNN and Transformer structures. This method can obtain a high-quality images from LDCT. A hybrid CNN-Transformer (HCformer) codec network model is proposed for LDCT image denoising. A neighborhood feature enhancement (NEF) module is designed to introduce the local information into the Transformer's operation, and the representation of adjacent pixel information in the LDCT image denoising task is increased. The shifting window method is utilized to lower the computational complexity of the network model and overcome the problems that come with computing the MSA (Multi-head self-attention) process in a fixed window. Meanwhile, W/SW-MSA (Windows/Shifted window Multi-head self-attention) is alternately used in two layers of the Transformer to gain the information interaction between various Transformer layers. This approach can successfully decrease the Transformer's overall computational cost. The AAPM 2016 LDCT grand challenge dataset is employed for ablation and comparison experiments to demonstrate the viability of the proposed LDCT denoising method. Per the experimental findings, HCformer can increase the image quality metrics SSIM, HuRMSE and FSIM from 0.8017, 34.1898, and 0.6885 to 0.8507, 17.7213, and 0.7247, respectively. Additionally, the proposed HCformer algorithm will preserves image details while it reduces noise. In this paper, an HCformer structure is proposed based on deep learning and evaluated by using the AAPM LDCT dataset. Both the qualitative and quantitative comparison results confirm that the proposed HCformer outperforms other methods. The contribution of each component of the HCformer is also confirmed by the ablation experiments. HCformer can combine the advantages of CNN and Transformer, and it has great potential for LDCT image denoising and other tasks. [ABSTRACT FROM AUTHOR]

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partially eliminates the local inductive bias in convolutional operations, thereby improving the accuracy of tasks such as segmentation and classification. Researchers have proposed various hybrid structures combining Transformer and Convolutional Neural Networks. One strategy is to stack Transformer blocks and convolutional blocks to concentrate on eliminating the accumulated local bias of convolutional operations. Another strategy is to nest convolutional blocks and Transformer blocks to eliminate bias within each nested block. However, due to the granularity of bias elimination operations, these two strategies cannot fully exploit the potential of Transformer. In this paper, a parallel hybrid model is proposed for segmentation, which includes a Transformer branch and a Convolutional Neural Network branch in encoder. After parallel feature extraction, inter-layer information fusion and exchange of complementary information are performed between the two branches, simultaneously extracting local and global features while eliminating the local bias generated by convolutional operations within the current layer. A pure convolutional operation is used in decoder to obtain final segmentation results. To validate the impact of the granularity of bias elimination operations on the effectiveness of local bias elimination, the experiments in this paper were conducted on Flare21 dataset and Amos22 dataset. The average Dice coefficient reached 92.65% on Flare21 dataset, and 91.61% on Amos22 dataset, surpassing comparative methods. The experimental results demonstrate that smaller granularity of bias elimination operations leads to better performance. [ABSTRACT FROM AUTHOR]

Fungal keratitis (FK) is a common and severe corneal disease, which is widely spread in tropical and subtropical areas. Early diagnosis and treatment are vital for patients, with confocal microscopy cornea imaging being one of the most effective methods for the diagnosis of FK. However, most cases are currently diagnosed by the subjective judgment of ophthalmologists, which is time-consuming and heavily depends on the experience of the ophthalmologists. In this paper, we introduce a novel structure-aware automatic diagnosis algorithm based on deep convolutional neural networks for the accurate diagnosis of FK. Specifically, a two-stream convolutional network is deployed, combining GoogLeNet and VGGNet, which are two commonly used networks in computer vision architectures. The main stream is used for feature extraction of the input image, while the auxiliary stream is used for feature discrimination and enhancement of the hyphae structure. Then, the features are combined by concatenating the channel dimension to obtain the final output, i.e., normal or abnormal. The results showed that the proposed method achieved accuracy, sensitivity, and specificity of 97.73%, 97.02%, and 98.54%, respectively. These results suggest that the proposed neural network could be a promising computer-aided FK diagnosis solution. [ABSTRACT FROM AUTHOR]

The external and middle ear conditions are diagnosed using a digital otoscope. The clinical diagnosis of ear conditions is suffered from restricted accuracy due to the increased dependency on otolaryngologist expertise, patient complaint, blurring of the otoscopic images, and complexity of lesions definition. There is a high requirement for improved diagnosis algorithms based on otoscopic image processing. This paper presented an ear diagnosis approach based on a convolutional neural network (CNN) as feature extraction and long short-term memory (LSTM) as a classifier algorithm. However, the suggested LSTM model accuracy may be decreased by the omission of a hyperparameter tuning process. Therefore, Bayesian optimization is used for selecting the hyperparameters to improve the results of the LSTM network to obtain a good classification. This study is based on an ear imagery database that consists of four categories: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). This study used 880 otoscopic images divided into 792 training images and 88 testing images to evaluate the approach performance. In this paper, the evaluation metrics of ear condition classification are based on a percentage of accuracy, sensitivity, specificity, and positive predictive value (PPV). The findings yielded a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a PPV of 100% for the testing database. Finally, the proposed approach shows how to find the best hyperparameters concerning the Bayesian optimization for reliable diagnosis of ear conditions under the consideration of LSTM architecture. This approach demonstrates that CNN-LSTM has higher performance and lower training time than CNN, which has not been used in previous studies for classifying ear diseases. Consequently, the usefulness and reliability of the proposed approach will create an automatic tool for improving the classification and prediction of various ear pathologies. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaques level, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images. The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods. [ABSTRACT FROM AUTHOR]

Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively., (© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Breast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from the activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to higher accuracy in the classification task and dimension reduction plays an important role. We have proposed reduced DeCAF (R-DeCAF) for this purpose, and different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. This framework uses pre-trained CNNs such as AlexNet, VGG-16, and VGG-19 as feature extractors in transfer learning mode. The DeCAF features are extracted from the first fully connected layer of the mentioned CNNs, and a support vector machine is used for classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to higher accuracy in the classification task using a small number of features considering a specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis and ICIAR datasets. Comprehensive results show improvement in the classification accuracy up to 4.3% with a feature vector size (FVS) of 23 and CEV equal to 0.15. [ABSTRACT FROM AUTHOR]

Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in this paper, which is used to provide a relatively effective balance between suppressing noise interference and enhancing feature learning. First, we design the dual-GAN backbone to effectively compensate for the spatial information damage caused by multiple pooling operations in the feature generation stage. Specifically, dual-GAN performs joint adversarial learning on the multiscale feature maps at the end of the generator, which yields an average Dice coefficient (DSC) gain of 5.95% over the baseline. Next, to suppress MRI high-frequency noise interference, a multilayer information constraint unit is introduced before feature decoding, which improves the sensitivity of the decoder to forecast features by 5.39% and effectively alleviates the network overfitting problem. Then, to refine the boundary segmentation effects, we construct a multiscale feature attention restraint mechanism, which forces the network to concentrate more on effective multiscale details, thus improving the robustness. Furthermore, the dual discriminators D1 and D2 also effectively prevent the negative migration phenomenon. The proposed DMCA-GAN obtained a DSC of 90.53% on the Medical Segmentation Decathlon (MSD) dataset with tenfold cross-validation, which is superior to the backbone by 3.78%. [ABSTRACT FROM AUTHOR]

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field. [ABSTRACT FROM AUTHOR]

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) distinguishing the characteristics of structures, textures, and noise confused in the image domain, and (2) representing local details and global semantics in the hierarchical features. In this paper, we propose a novel denoising method consisting of (1) a 2D dual-domain restoration framework to reconstruct noise-free structure and texture signals separately, and (2) a 3D multi-depth reinforcement U-Net model to further recover image details with enhanced hierarchical features. In the 2D dual-domain restoration framework, the convolutional neural networks are adopted in both the image domain where the image structures are well preserved through the spatial continuity, and the sinogram domain where the textures and noise are separately represented by different wavelet coefficients and processed adaptively. In the 3D multi-depth reinforcement U-Net model, the hierarchical features from the 3D U-Net are enhanced by the cross-resolution attention module (CRAM) and dual-branch graph convolution module (DBGCM). The CRAM preserves local details by integrating adjacent low-level features with different resolutions, while the DBGCM enhances global semantics by building graphs for high-level features in intra-feature and inter-feature dimensions. Experimental results on the LUNA16 dataset and 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset illustrate the proposed method outperforms the state-of-the-art methods on removing noise from LDCT images with clear structures and textures, proving its potential in clinical practice. [ABSTRACT FROM AUTHOR]

Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images. [ABSTRACT FROM AUTHOR]

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes. [ABSTRACT FROM AUTHOR]

Medical imaging has acquired more attention due to the emerging design of wireless technologies, the internet, and data storage. The reflection of these technologies has gained attraction in medicine and medical sciences facilitating the diagnosis and treatment of different diseases in an effective manner. However, medical images are vulnerable to noise, which can make the image unclear and perplex the identification. Thus, denoising of medical images is imperative for processing medical images. This paper devises a novel optimal deep convolution neural network-based vectorial variation (ODVV) filter for denoising medical computed tomography (CT) images and Lena images. Here, the input medical images are fed to a noisy pixel map identification module wherein the deep convolutional neural network (Deep CNN) is adapted for discovering noisy pixel maps. Here, Deep CNN training is done with the Adam algorithm. Once noisy pixels are identified, it is further given to noise removal module which is performed using the proposed optimization algorithm, namely Feedback Artificial Lion (FAL). Here, the FAL is devised by combining the FAT and Lion algorithm. After noise removal, the pixel enhancement is performed using the vectorial total variation norm to get final pixel-enhanced image. The proposed FAL algorithm offered enhanced performance in contrast to other techniques with the highest peak signal-to-noise ratio (PSNR) of 24.149 dB, highest second-derivative-like measure of enhancement (SDME) of 32.142 dB, highest structural index similarity (SSIM) of 0.800, and Edge Preserve Index (EPI) of 0.9267., (© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively., (© 2023. The Author(s).)

An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pathology, on top of the detection of its specific stage. Furthermore, the early detection of diabetic retinopathy and the follow-up of the patient's condition remains an arduous task, whether for an experienced expert ophthalmologist or a computer-aided diagnosis technician. In this paper, we aim to propose a new automatic diabetic retinopathy severity level detection method. The proposed approach merges the pyramid hierarchy of the discrete wavelet transform of the retina fundus image with the modified capsule network and the modified inception block proposed, in addition to a new deep hybrid model that concatenates the inception block with capsule networks. The performance of our proposed approach has been validated on the APTOS dataset, as it achieved a high training accuracy of 97.71% and a high testing accuracy score of 86.54%, which is considered one of the best scores achieved in this field using the same dataset. [ABSTRACT FROM AUTHOR]

If lung cancer is not detected in its initial phases, it can be fatal. However, because of the quantity and structure of its nodules, lung cancer is difficult to detect early. For accurate detections, radiologists require assistance from automated tools. Numerous expert methods have been created over time to assist radiologists in the diagnosis of lung cancer. However, this requires accurate research. Therefore, in this article, we propose a framework to precisely detect lung cancer by categorizing it between benign and malignant nodules. To achieve this objective, an efficient deep-learning algorithm is presented. The presented technique consists of four stages, namely pre-processing, segmentation, classification, and severity stage analysis. Initially, the collected image is given to the pre-processing stage to eliminate the distortion present in the image. Then, the noise-free image is given to the segmentation stage. For segmentation, in this paper, modified regularized K-means (MRKM) clustering algorithm is presented. After the segmentation process, the segmented nodule image is fed to the classification stage to categorize the nodule as benign or malignant (risk nodule). For classification, an improved convolution neural network (ICNN) is presented. The proposed ICNN is designed by modifying CNN with the integration of the adaptive tree seed optimization (ATSO) algorithm. Finally, the stage identification is carried out based on the size of the nodule and we classify the malignant nodule as S1–S4. The presented technique attained the maximum accuracy of 96.5% and performance compared with existing state-of-art methods. [ABSTRACT FROM AUTHOR]

Retinal fundus images are non-invasively acquired and faced with low contrast, noise, and uneven illumination. The low-contrast problem makes objects in the retinal fundus image indistinguishable and the segmentation of blood vessels very challenging. Retinal blood vessels are significant because of their diagnostic importance in ophthalmologic diseases. This paper proposes improved retinal fundus images for optimal segmentation of blood vessels using convolutional neural networks (CNNs). This study explores some robust contrast enhancement tools on the RGB and the green channel of the retinal fundus images. The improved images undergo quality evaluation using mean square error (MSE), peak signal to noise ratio (PSNR), Similar Structure Index Matrix (SSIM), histogram, correlation, and intersection distance measures for histogram comparison before segmentation in the CNN-based model. The simulation results analysis reveals that the improved RGB quality outperforms the improved green channel. This revelation implies that the choice of RGB to the green channel for contrast enhancement is adequate and effectively improves the quality of the fundus images. This improved contrast will, in turn, boost the predictive accuracy of the CNN-based model during the segmentation process. The evaluation of the proposed method on the DRIVE dataset achieves an accuracy of 94.47, sensitivity of 70.92, specificity of 98.20, and AUC (ROC) of 97.56., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

It is more than a decade since machine learning and especially its leading subtype deep learning have become one of the most interesting topics in almost all areas of science and industry. In numerous contexts, at least one of the applications of deep learning is utilized or is going to be utilized. Using deep learning for image classification is now very popular and widely used in various use cases. Many types of research in medical sciences have been focused on the advantages of deep learning for image classification problems. Some recent researches show more than 90% accuracy for breast tissue classification which is a breakthrough. A huge number of computations in deep neural networks are considered a big challenge both from software and hardware point of view. From the architectural perspective, this big amount of computing operations will result in high power consumption and computation runtime. This led to the emersion of deep learning accelerators which are designed mainly for improving performance and energy efficiency. Data reuse and localization are two great opportunities for achieving energy-efficient computations with lower runtime. Data flows are mainly designed based on these important parameters. In this paper, DLA-H and BJS, a deep learning accelerator, and its data flow for histopathologic image classification are proposed. The simulation results with the MAESTRO tool showed 756 cycles for total runtime and [Formula: see text] GFLOPS roofline throughput that is an extreme performance improvement in comparison to current general-purpose deep learning accelerators and data flows., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The interior reconstruction of completely truncated projection data is a frontier research hotspot in cone-beam computed tomography (CBCT) application. It is difficult to find a method with acceptable accuracy and high efficiency to solve it. Based on the simplified algebraic reconstruction technique (S-ART) algorithm and the filtered back projection (FBP) algorithm with the new filter, an efficient and feasible interior reconstruction algorithm is proposed in this paper. The algorithm uses the S-ART algorithm to quickly recover the complete projection data and then uses the new ramp filter which can suppress the high-frequency noise in the projection data to filter the recovered complete projection data. Finally, the interior reconstructed images are obtained by back projection. The computational complexity of the proposed algorithm is close to that of the FBP algorithm for the reconstruction of the whole object, and the reconstructed image quality is acceptable, which provides an effective method for interior reconstruction in CBCT. Simulation results show the effectiveness of the method., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

In this paper, we describe an algorithm for accurately segmenting multiple nudfclei from clumps of non-overlapping immuno-histochemically stained histological hepatic (liver) images. This problem is notoriously difficult because of the degree of presence of stains among the multi-nucleated cells, the poor contrast of cell cytoplasm, and the presence of mucus, blood, and inflammatory cells in the images. Hepatocellular carcinoma, characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multi-nucleation, poses a prominent threat. Our proposed method addresses the aforementioned issues for an automated diagnosis system by judging the presence of multiple nuclei in a two-step process: the Quickhull algorithm defines the convex hull of each cell in the image and candidate nuclei regions are located with morphological operations. A combination of features containing local minima and shape-dependent features is extracted for the detection of single or multiple nuclei in each cell with a significant reduction in the number of false positives and false negatives providing an accuracy of 89.76%., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarcinoma. 1431 clinical cases and a total of 1624 GGNs (3–30 mm) were collected from Shanghai Cancer Center for the study. The data is in high-resolution computed tomography (HRCT) format. Firstly, the automatic GGN detector which is composed by a 3D U-Net and a 3D multi-receptive field (multi-RF) network detects the location of GGNs. Then, a deep 3D convolutional neural network (3D-CNN) called Attention-v1 is used to identify the GGNs' invasiveness. The attention mechanism was introduced to the 3D-CNN. This paper conducted a contract experiment to compare the performance of Attention-v1, ResNet, and random forest algorithm. ResNet is one of the most advanced convolutional neural network structures. The competition performance metrics (CPM) of automatic GGN detector reached 0.896. The accuracy, sensitivity, specificity, and area under curve (AUC) value of Attention-v1 structure are 85.2%, 83.7%, 86.3%, and 92.6%. The algorithm proposed in this paper outperforms ResNet and random forest in sensitivity, accuracy, and AUC value. The deep 3D-CNN's classification result is better than traditional machine learning method. Attention mechanism improves 3D-CNN's performance compared with the residual block. The automatic GGN detector with the addition of Attention-v1 can be used to construct the GGN invasiveness classification algorithm to help the patients and doctors in treatment. [ABSTRACT FROM AUTHOR]

Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics. [ABSTRACT FROM AUTHOR]

Accurate registration of lung X-rays is an important task in medical image analysis. However, the conventional methods usually cost a lot in running time, and the existing deep learning methods are hard to deal with the large deformation caused by respiratory and cardiac motion. In this paper, we attempt to use deep learning methods to deal with large deformation and enable it to achieve the accuracy of conventional methods. We proposed the cascading affine and B-spline network (CABN), which consists of convolutional cross-stitch affine block (CCAB) and B-splines U-net-like block (BUB) for large lung motion. CCAB makes use of the convolutional cross-stitch model to learn global features among images. And BUB adopts the idea of cubic B-splines which is suitable for large deformation. We separately demonstrated CCAB, BUB, and CABN on two chest X-ray datasets. The experimental results indicate that our methods are highly competitive both in accuracy and runtime when compared to both other deep learning methods and iterative conventional approaches. Moreover, CCAB also can be used for the preprocessing of non-rigid registration methods, replacing affine in conventional methods. [ABSTRACT FROM AUTHOR]

In recent years, the quantity and complexity of medical imaging acquisition and processing have increased tremendously. The explosion in volume and need for advanced imaging analysis have led to the creation of numerous software programs, which have begun to be incorporated into clinical practice for indications such as automated stroke assessment, brain tumor perfusion processing, and hippocampal volume analysis. Despite these advances, there remains a need for specialized, custom-built software for advanced algorithms and new areas of research that is not widely available or adequately integrated in these "out-of-the-box" solutions. The purpose of this paper is to describe the implementation of an image-processing pipeline that is versatile and simple to create, which allows for rapid prototyping of image analysis algorithms and subsequent testing in a clinical environment. This pipeline uses a combination of Orthanc server, custom MATLAB code, and publicly available FMRIB Software Library and RestNeuMap tools to automatically receive and analyze resting-state functional MRI data collected from a custom filter on the MR scanner output. The processed files are then sent directly to Picture Archiving and Communications System (PACS) without the need for user input. This initial experience can serve as a framework for those interested in simple implementation of an automated pipeline customized to clinical needs. [ABSTRACT FROM AUTHOR]

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of 'unsharpness' in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Spleen tissue segmentation is an essential process for analyzing various immunological diseases as observed in the cryo-imaging data. Because manual labeling of the spleen tissue by human experts is not efficient, an automatic segmentation algorithm is needed. In this study, we developed a novel algorithm for automatically segmenting spleen substructures including white pulp and red pulp for the first time. The algorithm is designed for datasets created by a cryo-imaging system. This unique technology can effectively enable cellular tracking anywhere in the whole mouse with single-cell sensitivity. The proposed algorithm consists of four components: initial spleen mask creation, feature extraction, Supervised Patch-based Fuzzy c-Mean (spFCM) classification, and post-processing. The algorithm accurately and efficiently labeled spleen tissues in all experiment settings. The algorithm also improved the spleen segmentation throughput by 90 folds as compared to the manual segmentation. Moreover, we show that our novel spFCM algorithm outperformed traditional fast-learning classifiers as well as the U-Net deep-learning model in many aspects. Two major contributions of this paper are (1) an explainable algorithm for segmenting spleen tissues in cryo-images for the first time and (2) an spFCM algorithm as a new classifier. We also discussed that our work can be beneficial to researchers who work not only in the fields of graft-versus-host disease (GVHD) mouse models, but also in that of other immunological disease models where spleen analysis is essential. Future work building upon our research may lay the foundations for biomedical studies that utilize cryo-imaging technology. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging. [ABSTRACT FROM AUTHOR]

Medical image analysis for perfect diagnosis of disease has become a very challenging task. Due to improper diagnosis, required medical treatment may be skipped. Proper diagnosis is needed as suspected lesions could be missed by the physician's eye. Hence, this problem can be settled up by better means with the investigation of similar case studies present in the healthcare database. In this context, this paper substantiates an assistive system that would help dermatologists for accurate identification of 23 different kinds of melanoma. For this, 2300 dermoscopic images were used to train the skin-melanoma similar image search system. The proposed system uses feature extraction by assigning dynamic weights to the low-level features based on the individual characteristics of the searched images. Optimal weights are obtained by the newly proposed optimized pair-wise comparison (OPWC) approach. The uniqueness of the proposed approach is that it provides the dynamic weights to the features of the searched image instead of applying static weights. The proposed approach is supported by analytic hierarchy process (AHP) and meta-heuristic optimization algorithms such as particle swarm optimization (PSO), JAYA, genetic algorithm (GA), and gray wolf optimization (GWO). The proposed approach has been tested with images of 23 classes of melanoma and achieved significant precision and recall. Thus, this approach of skin melanoma image search can be used as an expert assistive system to help dermatologists/physicians for accurate identification of different types of melanomas. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method's qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy. This paper focuses on the segmentation of red lesions using nested U-Net Zhou et al. (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2018) followed by removal of false positives based on the sub-image classification method. Different sizes of sub-images were studied for the reduction in false positives in the sub-image classification method. The network could capture semantic features and fine details due to dense convolutional blocks connected via skip connections in between down sampling and up sampling paths. False-negative candidates were very few and the sub-image classification network effectively reduced the falsely detected candidates. The proposed framework achieves a sensitivity of 88.79 % , precision of 71.50 % , and F1-Score of 79.21 % for the DIARETDB1 data set Kalviainen and Uusutalo (Medical Image Understanding and Analysis, Citeseer, 2007). It outperforms the state-of-the-art networks such as U-Net Ronneberger et al. (International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015) and attention U-Net Oktay et al. (Attention u-net: Learning where to look for the pancreas, 2018). [ABSTRACT FROM AUTHOR]

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF–the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of y = A x + b , named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference—that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors—combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching–learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs. [ABSTRACT FROM AUTHOR]

Denoising, skull stripping, segmentation, feature extraction, and classification are five important processes in this paper's development of a brain tumor classification model. The brain tumor image will be imposed first using the entropy-based trilateral filter to de-noising and this image is imposed to skull stripping by means of morphological partition and Otsu thresholding. Adaptive contrast limited fuzzy adaptive histogram equalization (CLFAHE) is also used in the segmentation process. The gray-level co-occurrence matrix (GLCM) characteristics are derived from the segmented image. The collected GLCM features are used in a hybrid classifier that combines the neural network (NN) and deep belief network (DBN) ideas. As an innovation, the hidden neurons of the two classifiers are modified ideally to improve the prediction model's accuracy. The hidden neurons are optimized using a unique hybrid optimization technique known as lion with dragonfly separation update (L-DSU), which integrates the approaches from both DA and LA. Finally, the suggested model's performance is compared to that of the standard models concerning certain performance measures. [ABSTRACT FROM AUTHOR]

Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation method that combines image enhancement and attention-based dense (AD) U-Net network. The choroidal images obtained from optical coherence tomography (OCT) are pre-enhanced by algorithms that include flattening, filtering, and exponential and linear enhancement to reduce choroid-independent information. Experimental results obtained from 800 OCT B-scans of the choroid layers from both normal eyes and high myopia showed that image enhancement significantly increased the performance of ADU-Net, with an AUC of 99.51% and a DSC of 97.91%. The accuracy of segmentation using the ADU-Net method with image enhancement is superior to that of the existing networks. In addition, we describe some algorithms that can measure automatically choroidal foveal thickness and the volume of adjacent areas. Statistical analyses of the choroidal parameters variation indicated that compared with normal eyes, high myopia has a reduction of 86.3% of the choroidal foveal thickness and 90% of the adjacent volume. It proved that high myopia is likely to cause choroid layer attenuation. These algorithms would have wide application in the diagnosis and precaution of related fundus lesions caused by choroid thinning from high myopia in future studies. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of malignancy diagnosed in women. Through early detection and diagnosis, there is a great chance of recovery and thereby reduce the mortality rate. Many preliminary tests like non-invasive radiological diagnosis using ultrasound, mammography, and MRI are widely used for the diagnosis of breast cancer. However, histopathological analysis of breast biopsy specimen is inevitable and is considered to be the golden standard for the affirmation of cancer. With the advancements in the digital computing capabilities, memory capacity, and imaging modalities, the development of computer-aided powerful analytical techniques for histopathological data has increased dramatically. These automated techniques help to alleviate the laborious work of the pathologist and to improve the reproducibility and reliability of the interpretation. This paper reviews and summarizes digital image computational algorithms applied on histopathological breast cancer images for nuclear atypia scoring and explores the future possibilities. The algorithms for nuclear pleomorphism scoring of breast cancer can be widely grouped into two categories: handcrafted feature-based and learned feature-based. Handcrafted feature-based algorithms mainly include the computational steps like pre-processing the images, segmenting the nuclei, extracting unique features, feature selection, and machine learning–based classification. However, most of the recent algorithms are based on learned features, that extract high-level abstractions directly from the histopathological images utilizing deep learning techniques. In this paper, we discuss the various algorithms applied for the nuclear pleomorphism scoring of breast cancer, discourse the challenges to be dealt with, and outline the importance of benchmark datasets. A comparative analysis of some prominent works on breast cancer nuclear atypia scoring is done using a benchmark dataset which enables to quantitatively measure and compare the different features and algorithms used for breast cancer grading. Results show that improvements are still required, to have an automated cancer grading system suitable for clinical applications. [ABSTRACT FROM AUTHOR]

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the most widely found disease among women in the world. The early detection of BC can frequently lessen the mortality rate as well as progress the probability of providing proper treatment. Hence, this paper focuses on devising the Exponential Honey Badger Optimization-based Deep Covolutional Neural Network (EHBO-based DCNN) for early identification of BC in the Internet of Things (IoT). Here, the Honey Badger Optimization (HBO) and Exponential Weighted Moving Average (EWMA) algorithms have been combined to create the EHBO. The EHBO is created to transfer the acquired medical data to the base station (BS) by choosing the best cluster heads to categorize the BC. Then, the statistical and texture features are extracted. Further, data augmentation is performed. Finally, the BC classification is done by DCNN. Thus, the observational outcome reveals that the EHBO-based DCNN algorithm attained outstanding performance concerning the testing accuracy, sensitivity, and specificity of 0.9051, 0.8971, and 0.9029, correspondingly. The accuracy of the proposed method is 7.23%, 6.62%, 5.39%, and 3.45% higher than the methods, such as multi-layer perceptron (MLP) classifier, deep learning, support vector machine (SVM), and ensemble-based classifier. [ABSTRACT FROM AUTHOR]

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively. [ABSTRACT FROM AUTHOR]

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming. In this study, we propose a hybrid model using a genetic algorithm (GA) and a residual convolutional neural network (CNN), ResNet-50V2, to predict ALL using microscopy images available in ALL-IDB dataset. However, accurate prediction requires suitable hyperparameters setup, and tuning these values manually still poses challenges. Hence, this paper uses GA to find the best hyperparameters that lead to the highest accuracy rate in the models. Also, we compare the performance of GA hyperparameter optimization with Random Search and Bayesian optimization methods. The results show that GA optimization improves the accuracy of the classifier, obtaining 98.46% in terms of accuracy. Additionally, our approach sheds new perspectives on identifying leukemia based on computer vision strategies, which could be an alternative for applications in a real-world scenario. [ABSTRACT FROM AUTHOR]

Digital images used in the field of ophthalmology are among the most important methods for automatic detection of certain eye diseases. These processes include image enhancement as a primary step to assist optometrists in identifying diseases. Therefore, many algorithms and methods have been developed for the enhancement of retinal fundus images, which may experience challenges that typically accompany enhancement processes, such as artificial borders and dim lighting that mask image details. To eliminate these problems, a new algorithm is proposed in this paper based on separating colour images into three channels (red, green, and blue). The green channel is passed through a Wiener filter and reinforced using the CLAHE technique before merging with the original red and blue channels. Reducing the green channel noise with this approach is proven effective over the other colour channels. Results from the Contrast Improvement Index (CII) and linear index of fuzziness (r) test indicate the success of the proposed algorithm compared with alternate algorithms in the application of improving blood vessel imagery and other details within ten test fundus images selected from the DRIVER database., (© 2021. Society for Imaging Informatics in Medicine.)

The literature provides many works that focused on cell nuclei segmentation in histological images. However, automatic segmentation of bone canals is still a less explored field. In this sense, this paper presents a method for automatic segmentation approach to assist specialists in the analysis of the bone vascular network. We evaluated the method on an image set through sensitivity, specificity and accuracy metrics and the Dice coefficient. We compared the results with other automatic segmentation methods (neighborhood valley emphasis (NVE), valley emphasis (VE) and Otsu). Results show that our approach is proved to be more efficient than comparable methods and a feasible alternative to analyze the bone vascular network., (© 2021. Society for Imaging Informatics in Medicine.)

Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method. [ABSTRACT FROM AUTHOR]

Brain tissue segmentation in magnetic resonance imaging volumes is an important image processing step for analyzing the human brain. This paper presents a novel approach named Pseudo-Label Assisted Self-Organizing Map (PLA-SOM) that enhances the result produced by a base segmentation method. Using the output of a base method, PLA-SOM calculates pseudo-labels in order to keep inter-class separation and intra-class compactness in the training phase. For the mapping phase, PLA-SOM uses a novel fuzzy function that combines feature space learned by the SOM's prototypes, topological ordering from the map, and spatial information from a brain atlas. We assessed PLA-SOM performance on synthetic and real MRIs of the brain, obtained from the BrainWeb and the Internet Brain Image Repository datasets. The experimental results showed evidence of segmentation improvement achieved by the proposed method over six different base methods. The best segmentation improvements reported by PLA-SOM on synthetic brain scans are 11%, 6%, and 4% for the tissue classes cerebrospinal fluid, gray matter, and white matter, respectively. On real brain scans, PLA-SOM achieved segmentation enhancements of 15%, 5%, and 12% for cerebrospinal fluid, gray matter, and white matter, respectively. [ABSTRACT FROM AUTHOR]

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection. [ABSTRACT FROM AUTHOR]

Medical image data plays a critical role in health care today. Whatever the context of image processing (research and educational activities, diagnostic or forensic purposes, etc.), they are supposed to be treated as highly sensitive. Unfortunately, currently available image-processing tools also enable very sophisticated malicious modification of their content.This paper is focused on the assessment of the effectiveness of selected so-called zero watermarking methods (ones that do not cause any modification of an image sample), in the protection of the integrity, and proving authorship, of these medical image studies. We have studied many zero watermarking methods and selected one representative from each type of known algorithms for comparison.We have conducted a series of simulations over a huge research database of anonymized medical image studies (patient examinations).

The development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. For this task, fully convolutional network (FCN)-based architectures are used to build end-to-end segmentation solutions. In this paper, we proposed a multi-level Kronecker convolutional neural network (MLKCNN) that captures information at different levels to have both local and global level contextual information. Our ML-KCNN uses Kronecker convolution, which overcomes the missing pixels problem by dilated convolution. Moreover, we used a post-processing technique to minimize false positive from segmented outputs, and the generalized dice loss (GDL) function handles the data-imbalance problem. Furthermore, the combination of connected component analysis (CCA) with conditional random fields (CRF) used as a post-processing technique achieves reduced Hausdorff distance (HD) score of 3.76 on enhancing tumor (ET), 4.88 on whole tumor (WT), and 5.85 on tumor core (TC). Dice similarity coefficient (DSC) of 0.74 on ET, 0.90 on WT, and 0.83 on TC. Qualitative and visual evaluation of our proposed method shown effectiveness of the proposed segmentation method can achieve performance that can compete with other brain tumor segmentation techniques. [ABSTRACT FROM AUTHOR]