Search

Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA. [ABSTRACT FROM AUTHOR]

Intracranial hematoma, a severe brain injury caused by trauma or cerebrovascular disease, can result in blood accumulation and compression of brain tissue. Untreated cases can cause headaches, impaired consciousness, and even brain tissue damage or death. Therefore, early and accurate diagnosis is crucial. Traditional segmentation methods require physicians with extensive clinical experience and expertise to manually mark out the hematoma region, but for hematoma cases with irregular shapes and uneven grey levels, this process is cumbersome, and the segmentation results are not good. Existing deep learning‐based methods are more likely to perform binary segmentation, considering all hematomas as a class and segmenting them, but this segmentation cannot capture more detailed information and lacks the analysis of different types of hematomas. To address these problems, an ICH segmentation network combining CNN and Transformer Encoder is proposed for accurate segmentation of different types of hematomas. The network incorporated edge information and long‐range context into the segmentation process. Experimental results using the CQ500 dataset demonstrate comparable performance to existing methods, with mIoU (0.8705), TPR (0.9273), mAP (0.9300), and DSC (0.9286) as the best metrics achieved by this paper's method. [ABSTRACT FROM AUTHOR]

The complex nature of human brain tissues is important in ensuring accurate diagnosis to save human lives. Research on early detection of brain diseases has gained significant prominence within medical intelligence using highly complex model architectures with only a single label that cannot be verified. This paper introduces an innovative approach to Siamese Network Genetic Algorithm (SN‐GA) leveraging Siamese contrastive learning for classifying brain images across diverse diseases. Our core architecture is a Bi‐Convolutional Neural Network (Bi‐CNN) optimized by a genetic algorithm to enhance brain image classification. Specifically, five widely recognized transfer learning‐based architectures, namely AlexNet, Efficient‐B0, VGG‐16, ResNet‐50, and Inception‐v3, have been incorporated to evaluate the effectiveness of the proposed SN‐GA system. The performance of these models has been rigorously analyzed and compared using two distinct datasets: Brain tumors and Alzheimer's datasets. The experimental results robustly affirm the efficacy of the proposed Siamese model, yielding exceptional levels of accuracy, precision, and recall, all peaking at 97%. These findings underscore the potential and resilience of the optimized Siamese network in the context of brain disease classification, emphasizing its significance in advancing the field of medical imaging and diagnosis, with implications for early intervention and patient care. [ABSTRACT FROM AUTHOR]

Glaucoma poses a significant threat to vision, capable of causing irreversible damage and, in severe instances, leading to permanent blindness. Accurate optic cup (OC) and optic disc (OD) segmentation are essential in glaucoma screening. In this study, a novel OC and OD segmentation approach is proposed. Based on U‐Net, it is optimized by introducing cardinality dimensions. Moreover, attention gates are implemented to reinforce salient features while suppressing irrelevant information. Additionally, a convolutional block attention module (CBAM) is integrated into the decoder segment. This fusion hones in on effective information in both channel and spatial dimensions. Meanwhile, an image processing procedure is proposed for image normalization and enhancement. All of these increase the accuracy of the model. This model is evaluated on the ORIGA and REFUGE datasets, demonstrating the model's superiority in OC and OD segmentation compared to the state‐of‐the‐art methods. Additionally, after the proposed image processing, cup‐to‐disc ratio (CDR) prediction on a batch of 155 in‐house fundus images yields an absolute CDR error of 0.099, which is reduced by 0.04 compared to the case where only conventional processing was performed. [ABSTRACT FROM AUTHOR]

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation. [ABSTRACT FROM AUTHOR]

Chest X-ray imaging is a critical diagnostic tool for identifying pulmonary diseases. However, manual interpretation of these images is time-consuming and error-prone. Automated systems utilizing convolutional neural networks (CNNs) have shown promise in improving the accuracy and efficiency of chest X-ray image classification. While previous work has mainly focused on using feature maps from the final convolution layer, there is a need to explore the benefits of leveraging additional layers for improved disease classification. Extracting robust features from limited medical image datasets remains a critical challenge. In this paper, we propose a novel deep learning-based multilayer multimodal fusion model that emphasizes extracting features from different layers and fusing them. Our disease detection model considers the discriminatory information captured by each layer. Furthermore, we propose the fusion of different-sized feature maps (FDSFM) module to effectively merge feature maps from diverse layers. The proposed model achieves a significantly higher accuracy of 97.21% and 99.60% for both three-class and two-class classifications, respectively. The proposed multilayer multimodal fusion model, along with the FDSFM module, holds promise for accurate disease classification and can also be extended to other disease classifications in chest X-ray images. [ABSTRACT FROM AUTHOR]

In this study, a new approach is presented that eliminates stereotactic frameworks and the use of markers, offering an alternative to traditional brain biopsy systems. The classical biopsy operation involves the registration of magnetic resonance (MR) and computed tomography (CT) information taken from the patient at different times. Typically, the surgeon's planning information, which takes an average of 4 h on MR, is transferred to CT, and the surgical operation commences. However, this approach necessitates two separate acquisitions (MR and CT), adversely affecting patient comfort and increasing the workload. In the proposed system, it is recommended to register MR‐Depth camera data instead of MR‐CT registration. To achieve this, a 3D face pattern is obtained from the data received from the depth camera attached to the robot arm and overlapped with the mesh obtained by segmentation of the MR. It was observed that registration with sub‐millimeter precision was achieved using the CMFreg surface registration technique. [ABSTRACT FROM AUTHOR]

Background: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. Purpose: To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). Methods: This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index. Results: A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p <.00001 in both cohorts). Conclusions: Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice. Clinical relevance statement: Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards. Key Points: • Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort. [ABSTRACT FROM AUTHOR]

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. Time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing. [ABSTRACT FROM AUTHOR]

Recent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation. [ABSTRACT FROM AUTHOR]

Automatic segmentation of renal tumours during renal cell carcinoma surgery can help doctors accurately locate the tumour region, protect the tissues and organs around the kidneys, enhance surgical efficiency, and reduce the possibility of leakage and misdiagnosis. However, since general polyp endoscopic image segmentation models have many problems when facing the task of renal tumour segmentation, there needs to be more research on the segmentation of endoscopic images of renal tumours. This paper proposes a multi‐scale dynamic segmentation kernel network for endoscopic image segmentation of kidney tumours. First, a spatial receptive field module is proposed to augment the feature information and improve the performance of the whole network. Second, an enhanced cross‐attention module is offered to attenuate the effect of a high‐similarity segmentation background. Finally, a multi‐scale dynamic segmentation kernel module is introduced to gradually refine the segmentation results from small to large sizes to obtain more accurate tumour boundaries. Extensive experiments on the established kidney tumour endoscopic dataset and publicly available endoscopic datasets show that this method exhibits enhanced performance and generalization capabilities compared to existing techniques. On this renal tumour dataset, MDSK‐Net achieved excellent results of 94.1% and 90.1% on mDice and mIoU. [ABSTRACT FROM AUTHOR]

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. OBJECTIVE: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. METHODS: The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. RESULTS: Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. CONCLUSION: The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions. [ABSTRACT FROM AUTHOR]

Today, with the expansion of technology and new architectures of deep learning, the accuracy of artificial intelligence methods in diagnosing diseases has increased. On the other hand, with the spread of new pandemic diseases such as Covid-19, timely and accurate diagnosis of the disease has become more important. Recently, proposed deep learning methods diagnose Covid-19 with acceptable accuracy but have expensive computational cost which could not distributed and implemented in edge devices. Sometimes the type of disease could be diagnosed by small models with few parameters. These small models can be placed in the fog or edge devices, and if they detect the disease with high confidence locally, the disease investigation request will not be sent to the cloud where the comprehensive and main trained model is located. Based on this idea; we proposed an ensemble of two deep learning models using boosting Shema named mobile COVID-Net, first a light weight MobileNet model designed and embedded in fog devices to diagnose pneumonia and Covid-19 which have similar symptoms with low computational cost and high confidence. If the embedded model fails to diagnose; a modified ResNet based neural network in the second layer designed to diagnose only Covid-19 with high precision in cloud, the distributed edge to cloud ensemble of neural network models trained and tested on publicly available dataset, has achieved a total accuracy of 93.8% for detection of Covid-19, in compare to 92.4% and 92% accuracy of COVID-Net and inception algorithms respectively. The most challenging part of the work is the accurate diagnosis of Covid-19 and pneumonia diseases from one another with the least amount of error. [ABSTRACT FROM AUTHOR]

Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed. [ABSTRACT FROM AUTHOR]

This research investigates the Circle of Willis, a critical vascular structure vital for cerebral blood supply. A modified novel dual‐pathway multi‐scale hierarchical upsampling network (HUNet) is presented, tailored explicitly for accurate segmentation of Circle of Willis anatomical components from medical imaging data. Evaluating both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, HUNet consistently outperforms the convolutional U‐net model, demonstrating superior capabilities and achieving higher accuracy across various classes. Additionally, the HUNet model achieves an exceptional dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 in both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, respectively. These metrics highlight HUNet's exceptional performance in achieving precise and accurate segmentation of anatomical structures within the Circle of Willis, underscoring its robustness in medical image segmentation tasks. Visual representations substantiate HUNet's efficacy in delineating Circle of Willis structures, offering comprehensive insights into its superior performance. [ABSTRACT FROM AUTHOR]

Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated. [ABSTRACT FROM AUTHOR]

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area. [ABSTRACT FROM AUTHOR]

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494. [ABSTRACT FROM AUTHOR]

BACKGROUND: The rapid development of deep learning techniques has greatly improved the performance of medical image segmentation, and medical image segmentation networks based on convolutional neural networks and Transformer have been widely used in this field. However, due to the limitation of the restricted receptive field of convolutional operation and the lack of local fine information extraction ability of the self-attention mechanism in Transformer, the current neural networks with pure convolutional or Transformer structure as the backbone still perform poorly in medical image segmentation. METHODS: In this paper, we propose FDB-Net (Fusion Double Branch Network, FDB-Net), a double branch medical image segmentation network combining CNN and Transformer, by using a CNN containing gnConv blocks and a Transformer containing Varied-Size Window Attention (VWA) blocks as the feature extraction backbone network, the dual-path encoder ensures that the network has a global receptive field as well as access to the target local detail features. We also propose a new feature fusion module (Deep Feature Fusion, DFF), which helps the image to simultaneously fuse features from two different structural encoders during the encoding process, ensuring the effective fusion of global and local information of the image. CONCLUSION: Our model achieves advanced results in all three typical tasks of medical image segmentation, which fully validates the effectiveness of FDB-Net. [ABSTRACT FROM AUTHOR]

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods. [ABSTRACT FROM AUTHOR]

The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index). [ABSTRACT FROM AUTHOR]

Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify. Convolutional neural networks, an effective method in medical image processing, often ignore the relationship between lesions, leading to topological errors during training. To tackle topological errors, move is made from pixel‐level to hypergraph representations. Hypergraphs can model lesions as vertices connected by hyperedges, capturing the topology between lesions. This paper introduces a novel dynamic hypergraph learning strategy called DHLS. DHLS allows for the dynamic construction of hypergraphs contingent upon input vertex variations. A hypergraph global‐aware segmentation network, termed HGSNet, is further proposed. HGSNet can capture the key high‐order structure information, which is able to enhance global topology expression. Additionally, a composite loss function is introduced. The function emphasizes the global aspect and the boundary of segmentation regions. The experimental setup compared HGSNet with other advanced models on medical image datasets from various organs. The results demonstrate that HGSNet outperforms other models and achieves state‐of‐the‐art performance on three public datasets. [ABSTRACT FROM AUTHOR]

Segmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net. [ABSTRACT FROM AUTHOR]

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736–0.903), along with an F1 score of 0.798 (0.594–0.905), precision of 0.806 (0.596–0.896), recall of 0.830 (0.650–0.946), and a Cohen's Kappa (κ) of 0.708 (0.562–0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes. [ABSTRACT FROM AUTHOR]

Over the past few years, the COVID‐19 virus has had a significant impact on the physical and mental health of people around the world. Therefore, in order to effectively distinguish COVID‐19 patients, many deep learning efforts have used chest medical images to detect COVID‐19. As with model accuracy, interpretability is also important in the work related to human health. This work introduces an interpretable vision transformer that uses the prototype method for the detection of positive patients with COVID‐19. The model can learn the prototype features of each category based on the structural characteristics of ViT. The predictions of the model are obtained by comparing all the features of the prototype in the designed prototype block. The proposed model was applied to two chest X‐ray datasets and one chest CT dataset, achieving classification performance of 99.3%, 96.8%, and 98.5% respectively. Moreover, the prototype method can significantly improve the interpretability of the model. The decisions of the model can be interpreted based on prototype parts. In the prototype block, the entire inference process of the model can be shown and the predictions of the model can be demonstrated to be meaningful through the visualization of the prototype features. [ABSTRACT FROM AUTHOR]

Combining the extracted tongue features with other medical indicators can effectively judge the diseases of patients. The previous work usually only analyzes a certain feature of the tongue body and is unable to extract multiple features simultaneously. In this study, a multi‐label classification network named TIM‐Net is proposed, which integrates global and local features to achieve multi‐label intelligent diagnosis of Chinese medicine tongue images. First, a feature extraction network based on ResNet is proposed to capture the features of tongue images more sufficiently. Then, a multi‐label classification algorithm fusing global and local features is proposed, and targeted screening operations are carried out on the class‐related feature maps based on global confidence. In addition, a logical masking algorithm is proposed to ensure that the local features can only correct the feature labels they represent, and do not interfere with other feature labels. The classification accuracy is further improved by using local feature confidence and correcting the global classification results. Finally, the experimental results indicate that the classification accuracy of the tongue images is gradually improved through optimizing the feature extraction network and fusing local features, and it exceeds other state‐of‐the‐art multi‐label classification networks. [ABSTRACT FROM AUTHOR]

This paper emphasizes on detection and classification of Fluoro-Deoxy-Glucose (FDG) radioactivity uptakes in fused Positron Emission Tomography / Computerized Tomography (PET/CT) images automatically. The deep learning technique using Convolutional Neural Network (CNN) is proposed to reduce the complexity in observation, to solve the problem of low accurateness and the time-consuming process of traditional classification methods. The CNN layers are designed and proposed for the FDG uptakes classification problem in fused PET/CT images. The proposed modified CNN model is trained using different optimizers such as Stochastic Gradient Descent Momentum (SGDM), Adaptive Moment Estimation (ADAM), and Root Mean Square propagation (RMSprop). The deep features extracted from the proposed CNN are classified using different classifiers such as K-Nearest Neighbor (KNN), Decision Trees (DT), Ensemble, Naive Bayes (NB), and multi-class Support Vector Machine (SVM) the results of which are compared. The multi-class SVM classifier trained using SGDM optimizer attains the maximum test accuracy of 98.18% and was found to be superior to pre-trained deep models such as AlexNet, ResNet, and GoogleNet. [ABSTRACT FROM AUTHOR]

Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system's classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process. [ABSTRACT FROM AUTHOR]

Skin lesion segmentation from dermatoscopic images is essential for the diagnosis of skin cancer. However, it is still a challenging task due to the ambiguity of the skin lesions, the irregular shape of the lesions and the presence of various interfering factors. In this paper, we propose a novel Ambiguous Context Enhanced Attention Network (ACEANet) based on the classical encoder-decoder architecture, which is able to accurately and reliably segment a variety of lesions with efficiency. Specifically, a novel Ambiguous Context Enhanced Attention module is embedded in the skip connection to augment the ambiguous boundary information. A Dilated Gated Fusion block is employed in the end of the encoding phase, which effectively reduces the loss of spatial location information due to continuous downsampling. In addition, we propose a novel Cascading Global Context Attention to fuse feature information generated by the encoder with features generated by the decoder of the corresponding layer. In order to verify the effectiveness and advantages of the proposed network, we have performed comparative experiments on ISIC2018 dataset and PH2 dataset. Experiments results demonstrate that the proposed model has superior segmentation performance for skin lesions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cardiovascular diseases are the top cause of death in China. Manual segmentation of cardiovascular images, prone to errors, demands an automated, rapid, and precise solution for clinical diagnosis. OBJECTIVE: The paper highlights deep learning in automatic cardiovascular image segmentation, efficiently identifying pixel regions of interest for auxiliary diagnosis and research in cardiovascular diseases. METHODS: In our study, we introduce innovative Region Weighted Fusion (RWF) and Shape Feature Refinement (SFR) modules, utilizing polarized self-attention for significant performance improvement in multiscale feature integration and shape fine-tuning. The RWF module includes reshaping, weight computation, and feature fusion, enhancing high-resolution attention computation and reducing information loss. Model optimization through loss functions offers a more reliable solution for cardiovascular medical image processing. RESULTS: Our method excels in segmentation accuracy, emphasizing the vital role of the RWF module. It demonstrates outstanding performance in cardiovascular image segmentation, potentially raising clinical practice standards. CONCLUSIONS: Our method ensures reliable medical image processing, guiding cardiovascular segmentation for future advancements in practical healthcare and contributing scientifically to enhanced disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer's disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score. [ABSTRACT FROM AUTHOR]

Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. Methods: In this retrospective study, CT images of 1070 T3–4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. Results: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments. Conclusion: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques. [ABSTRACT FROM AUTHOR]

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection. [ABSTRACT FROM AUTHOR]

Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the small scale of medical datasets, a limitation directly stemming from current medical data acquisition capabilities. To this end, we introduce AttEUnet, a medical cell segmentation network enhanced by edge attention, based on the Attention U-Net architecture. It incorporates a detection branch enhanced with edge attention and a learnable fusion gate unit to improve segmentation accuracy and convergence speed on small medical datasets. The AttEUnet allows for the integration of various types of prior information into the backbone network according to different tasks, offering notable flexibility and generalization ability. This method was trained and validated on two public datasets, MoNuSeg and PanNuke. The results show that AttEUnet significantly improves segmentation performance on small medical datasets, especially in capturing edge details, with F1 scores of 0.859 and 0.888 and Intersection over Union (IoU) scores of 0.758 and 0.794 on the respective datasets, outperforming both convolutional neural networks (CNNs) and transformer-based baseline networks. Furthermore, the proposed method demonstrated a convergence speed over 10.6 times faster than that of the baseline networks. The edge attention branch proposed in this study can also be added as an independent module to other classic network structures and can integrate more attention priors based on the task at hand, offering considerable scalability. [ABSTRACT FROM AUTHOR]

The intricate architecture of gastric anatomy coupled with the complexities inherent in esophagogastroduodenoscopy (EGD) procedures can lead to blind spots during examinations. These blind spots refer to anatomical locations not visualized during EGD examinations, potentially impacting timely diagnoses and treatments, and exacerbating patient conditions. Therefore, developing artificial intelligence (AI) for monitoring and reducing blind spots in EGD examinations is crucial. This study introduces MMCNet, a novel deep-learning model for classifying anatomical locations in EGD images. The model-based AI system can promptly alert the physician when it fails to recognize all anatomical locations in real-time EGD examinations, thereby enabling the monitoring and reduction of blind spots. To validate its efficacy, comprehensive experimental assessments compared MMCNet with established deep learning models. The results confirm MMCNet’s high accuracy rate of 97.25% in recognizing anatomical locations in EGD images. Moreover, its notably compact memory size of 4.16M contributes to reduced memory requirements. With its accuracy and small model size, the model demonstrates significant potential as an effective tool for computer-assisted blind spot detection. Additionally, this study presents a comprehensive workflow for applying deep learning models to address practical issues, which can be easily adapted for similar tasks. [ABSTRACT FROM AUTHOR]

Introduction: We present a user-friendly and robust system for the analysis of medical images, developed in Larazus, called SYFANMI, which is also available in Spanish as SIPANIME (Sistema para la Visualization de Imágenes Médicas). SYFANMI highlights the medical anomalies in images to support the specialist in decision-making and increase the diagnostic efficiency in bone early metastatic disease, osteoporosis, degenerative bone diseases, structural changes, etc. Objective: We aim for SYFANMI to be considered a computer-aided design (CAD) tool for the detection of bone anomalies (bone metastasis), supporting the expert during the clinical/medical analysis. Materials and methods: Development of SYFANMI as the integration of several functions (pixels switch-off, image transform, intensity profile, false color, histogram, segmentation, etc.) in Lazarus is to determine bone anomalies in radiography, mammography, echography, bone scan in several formats such as jpg, jpeg, bmp, and png. Results: We tested the use feasibility of the software as a support in the visualization of bone anomalies and early metastatic disease in medical images, allowing the specialist to verify the absence, presence, and/or progress of bone diseases. Conclusions: We successfully showed the use of SYFANMI as a low-cost multi-format CAD with the capability of adding new functions according to the place where it is used (medical center, low-resource oncological unit) focusing on the search and follow-up of bone metastasis. [ABSTRACT FROM AUTHOR]

Melanoma, a widespread and hazardous form of cancer, has prompted researchers to prioritize dermoscopic image‐based algorithms for classifying skin lesions. Recently, there has been a growing trend in using pre‐trained convolutional neural networks for detecting skin lesions. However, the features extracted from these classifiers may include irrelevant elements, emphasizing the importance of implementing effective feature selection methods. Nevertheless, there has not been a comprehensive study on feature selection methods to enhance the performance of skin lesion detection to date. To identify the most efficient methods, a diverse set of feature selection techniques, including filter, wrapper, embedded, and dimensionality reduction, were applied to images from two well‐known datasets, namely ISIC 2017 and ISIC 2018. According to the results, models trained with features chosen by wrapper techniques outperformed those trained with features chosen by filter and embedded methods. Achieving an accuracy of 0.8333 and an F1‐Score of 0.8291 for the ISIC 2017 dataset, and an F1‐Score of 0.9324 and an accuracy of 0.9350 for the ISIC 2018 dataset, the classification using features obtained via the GWO feature selection technique performed the best. [ABSTRACT FROM AUTHOR]

The automatic segmentation of brain tumours is a critical task in patient disease management. It can help specialists easily identify the location, size, and type of tumour to make the best decisions regarding the patients' treatment process. Recently, deep learning methods with attention mechanism helped increase the performance of segmentation models. The proposed method consists of two main parts: the first part leverages a deep neural network architecture for biggest tumour detection (BTD) and in the second part, ResNet152V2 makes it possible to segment the image with the attention block and the extraction of local and global features. The custom attention block is used to consider the most important parts in the slices, emphasizing on related information for segmentation. The results show that the proposed method achieves average Dice scores of 0.81, 0.87 and 0.91 for enhancing core, tumour core and whole tumour on BraTS2020 dataset, respectively. Compared with other segmentation approaches, this method achieves better performance on tumour core and whole tumour. Further comparisons on BraTS2018 and BraTS2017 validation datasets show that this method outperforms other models based on Dice score and Hausdorff criterion. [ABSTRACT FROM AUTHOR]

Deep learning‐based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi‐scale complexity‐aware registration network (MSCAReg‐Net) was proposed by devising a complexity‐aware technique to facilitate DLIR under a single‐resolution framework. Specifically, the complexity‐aware technique devised a multi‐scale complexity‐aware module (MSCA‐Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC‐Module) and a feature aggregation module (FA‐Module) to facilitate the MSCA‐Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg‐Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non‐positive Jacobian determinant (|Jϕ|≤0$| {{J_\phi }} | \le 0$), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi‐atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, |Jϕ|≤0$| {{J_\phi }} | \le 0$, and the downstream application on MAHS demonstrated the advances of this method. [ABSTRACT FROM AUTHOR]

In this work, we propose a morphologic analysis by means of the construction of 3D models of the SARS-CoV-2 VP (viral particles) with algorithms in Python and Matlab based on the processing of frames. To this aim, we simulate the Takeda method to induce periodicity and apply the Fourier transform to obtain the phase of objects under analysis. To this aim, we analyze several research works focused on infected tissues by SARS-CoV-2 virus culture cells, highlighting the obtained medical images of the virus from microscopy and tomography. We optimize the results by performing image processing (segmentation and periodic noise removal) in order to obtain an accurate ROI (Region of Interest) segmentation containing only information on SARS-CoV-2 cells. We apply our algorithm to these images (3D tomographic medical images) to simulate the Takeda method (which also filters the image), considering the periodicity induced by us in the image to carry out a phase unwrapping process. Finally, we use the image phase to focus on the body, center (RNA, Protein M-N), and spikes (Protein S) of the SARS-CoV-2 cells to identify them as characteristic elements of the SARS-CoV-2 virion morphology to build a 3D model based only in the metadata of clinical studies on cell cultures. The latter results in the construction of a mathematical, physical, biological, and numerical model of the SARS-CoV-2 virion, a tool with volumes, or 3D non-speculative or animated models, based only on medical images (3D tomography) in clinical tests, faithful to the virus. [ABSTRACT FROM AUTHOR]

Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease. [ABSTRACT FROM AUTHOR]

Using deep learning technology to segment oral CBCT images for clinical diagnosis and treatment is one of the important research directions in the field of clinical dentistry. However, the blurred contour and the scale difference limit the segmentation accuracy of the crown edge and the root part of the current methods, making these regions become difficult-to-segment samples in the oral CBCT segmentation task. Aiming at the above problems, this work proposed a Difficult-to-Segment Focus Network (DSFNet) for segmenting oral CBCT images. The network utilizes a Feature Capturing Module (FCM) to efficiently capture local and long-range features, enhancing the feature extraction performance. Additionally, a Multi-Scale Feature Fusion Module (MFFM) is employed to merge multiscale feature information. To further improve the loss ratio for difficult-to-segment samples, a hybrid loss function is proposed, combining Focal Loss and Dice Loss. By utilizing the hybrid loss function, DSFNet achieves 91.85% Dice Similarity Coefficient (DSC) and 0.216 mm Average Symmetric Surface Distance (ASSD) performance in oral CBCT segmentation tasks. Experimental results show that the proposed method is superior to current dental CBCT image segmentation techniques and has real-world applicability. [ABSTRACT FROM AUTHOR]

Due to the large fluctuations of the boundaries and internal variations of the lesion regions in medical image segmentation, current methods may have difficulty capturing sufficient global contexts effectively to deal with these inherent challenges, which may lead to a problem of segmented discrete masks undermining the performance of segmentation. Although self‐attention can be implemented to capture long‐distance dependencies between pixels, it has the disadvantage of computational complexity and the global contexts extracted by self‐attention are still insufficient. To this end, the authors propose the GFRNet, which resorts to the idea of low‐rank matrix factorization by forming global contexts locally to obtain global contexts that are totally different from contexts extracted by self‐attention. The authors effectively integrate the different global contexts extract by self‐attention and low‐rank matrix factorization to extract versatile global contexts. Also, to recover the spatial contexts lost during the matrix factorization process and enhance boundary contexts, the authors propose the Modified Matrix Decomposition module which employ depth‐wise separable convolution and spatial augmentation in the low‐rank matrix factorization process. Comprehensive experiments are performed on four benchmark datasets showing that GFRNet performs better than the relevant CNN and transformer‐based recipes. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) can cause irreversible eye damage, even blindness. The prognosis improves with early diagnosis. According to the International Classification of Diabetic Retinopathy Severity Scale (ICDRSS), DR has five stages. Modern, cost‐effective techniques for automatic DR screening and staging of fundus images are based on deep learning (DL). To obtain higher classification accuracy, the combination of several diverse individual DL models into one ensemble could be used. A new approach to model diversity in an ensemble is proposed by manipulating the training input data involving original and four variants of preprocessed image datasets. There are publicly available datasets with labels for all five stages, but some contain poor‐quality images. In contrast, this algorithm was trained on images from a six‐class DDR dataset, including the class of poor‐quality ungradable images, to enhance the classification performance. The solution was evaluated on the APTOS dataset, containing only ICDRSS classes. Classification results of the ensemble model were presented on two different ensemble convolutional neural network (CNN) models, based on Xception and EfficientNetB4 architectures using two fusion approaches. Our proposed ensemble models outperformed all other single deep learning architectures regarding overall accuracy and Cohen's Kappa, with the best results using the EfficientNetB4 architecture. [ABSTRACT FROM AUTHOR]

Neuron localization is a fundamental step in the neuron morphology reconstruction and quantitative analysis of cell counting and spatial distribution. The recent development of labelling and imaging techniques has resulted in growing demand for automatic neuron localization methods of 3D neuronal microscopy images. However, accurately localizing touching neurons remains a challenge. To address this issue, a novel method utilizing a super‐resolution segmentation network based on 2D Conv‐LSTM and a region growing method are proposed. This network can map and detect individual neurons in higher resolution space, allowing for the separation of closely touching neurons with reduced resource consumption. Subsequently, a region growing method is employed to localize neurons accurately. This method is evaluated using neuronal images generated by TDI‐fMOST. This method achieved neuron localization with an F1 score of 0.91. In comparison, other automatic localization methods achieved F1 scores lower than 0.85. It is also demonstrated that our network has fewer computational requirements compared to 3D neural networks. This method is promising for accurately localizing neurons in large‐scale neuron images. [ABSTRACT FROM AUTHOR]