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Background/Objectives: Computer-aided detection (CAD) systems for lung nodule detection often face challenges with 5 mm computed tomography (CT) scans, leading to missed nodules. This study assessed the efficacy of a deep learning-based slice thickness reduction technique from 5 mm to 1 mm to enhance CAD performance. Methods: In this retrospective study, 687 chest CT scans were analyzed, including 355 with nodules and 332 without nodules. CAD performance was evaluated on nodules, to which all three radiologists agreed. Results: The slice thickness reduction technique significantly improved the area under the receiver operating characteristic curve (AUC) for scan-level analysis from 0.867 to 0.902, with a p-value < 0.001, and nodule-level sensitivity from 0.826 to 0.916 at two false positives per scan. Notably, the performance showed greater improvements on smaller nodules than larger nodules. Qualitative analysis confirmed that nodules mistaken for ground glass on 5 mm scans could be correctly identified as part-solid on the refined 1 mm CT, thereby improving the diagnostic capability. Conclusions: Applying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT scans, supporting the clinical adoption of refined 1 mm CT scans for more accurate diagnoses. [ABSTRACT FROM AUTHOR]

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility. [ABSTRACT FROM AUTHOR]

Many people are affected by lung cancer and it kills a lot of people worldwide. Often times, lung cancer does not have symptoms in the early stage. Abnormal growths called pulmonary nodules may be found in the lungs. Some of the pulmonary nodules may be cancerous. So, identifying pulmonary nodules will help detect lung cancer at an early stage. V-3DResNets based on variants of residual network is proposed to improve the generalization capability of the pulmonary nodule detection system. A U-shaped encoder-decoder is used to effectively extract the features. Residual networks help to reduce the vanishing gradient problem in deep neural networks. A Slice Grouped Non Local (SGNL) module studies the relation among entities across Computed Tomography (CT) slices. The content-aware mechanism of the squeeze and excitation block assigns different weights to the channels and improves the performance of the nodule detection system. The proposed work is analyzed using the PN9 dataset that includes 8,798 CT Scans with nine types of pulmonary nodules which attained a Competition Performance metric (CPM) of 65.51%. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers, and most patients present with advanced or irresectable disease due to late recognition. Radiological imaging modalities such as CT scans are key in providing information on the presence or absence of tumors. However, an assessment of pancreatic cancer requires specific radiological expertise, and small tumors are easily overlooked. Computer-aided detection (CAD) using artificial intelligence (AI) techniques is promising and may help in the early detection of pancreatic tumors. In this study, we developed a deep learning-based tumor detection framework that can detect pancreatic head cancer on CT scans with high accuracy when incorporating clinically relevant information. We demonstrate that a tumor detection framework utilizing CT scans and secondary signs of pancreatic tumors results in an increased detection accuracy for the detection of pancreatic head tumors. The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy. [ABSTRACT FROM AUTHOR]

This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clinical trials for lung cancer screening, AI-CAD exhibited performance comparable to that of medical professionals in terms of sensitivity and specificity. Studies revealed that applying AI-CAD for diagnosing pulmonary metastases led to high detection rates. The use of a nodule matching algorithm in diagnosing pulmonary metastases significantly reduced false non-metastasis results. In clinical settings, implementing AI-CAD enhanced the efficiency of pulmonary nodule detection, saving time and effort during CT reading. Overall, AI-CAD is expected to offer substantial support for lung cancer screening and the interpretation of chest CT scans for malignant tumor surveillance. [ABSTRACT FROM AUTHOR]

Lung cancer poses a significant threat, contributing significantly to cancer-related mortality. Computer-aided detection plays a pivotal role, particularly in the automated identification of pulmonary nodules, assisting radiologists in diagnosis. Despite the remarkable efficacy of deep convolutional neural networks in lesion identification, the detection of small nodules remains an enduring challenge. A conventional automated detection framework encompasses two critical stages: candidate detection and false positive reduction. This study introduces a novel approach named ReRointNet, focusing on meticulous lung nodule localization and detection through strategically placed sample points. To enhance nodule detection, we propose integrating PointNet anchors with RPN anchors. PointNet, operating on local key points, facilitates this integration. The synergy achieved by merging these anchors within our RePointNet framework enhances nodule detection rates and substantially improves localization accuracy. Post-detection, identified nodules undergo classification using the 3D Convolutional Neural Networks (CNN) method. Our contribution presents a novel paradigm for nodule detection in lung Computed Tomography (CT) images, with reduced computational costs and improved memory efficiency. The combined utilization of RePointNet and 3DCNN demonstrates proficiency in identifying nodules of various sizes, including small nodules. Our research underscores the superiority of lung nodule identification through the utilization of RePointNet based on point information, surpassing conventional networks. Rigorous evaluations of the LUNA16 dataset reveal our method's superior performance compared to state-of-the-art systems, achieving a notable sensitivity of 91.6 percent at a speed of 0.9 frames per second. These findings underscore the potential of our proposed approach in advancing precise lung nodule diagnosis, offering invaluable support to healthcare practitioners and radiologists engaged in diagnosing lung cancer patients. [ABSTRACT FROM AUTHOR]

Precise and unambiguous segmentation of pulmonary nodules from the CT images is imperative for a CAD framework implementation delineated for the prognosis of lung cancer. Lung nodule segmentation is an appealing research discipline for accurate dismemberment of lung cancer but the irregularity in shades, contours, and compositions, and the affinity between the tumors and the neighboring regions makes it an arduous task. This paper proffers a series atrous convolution enhanced U‐Net which uses a series of concatenated dilated convolution blocks after every stage in the encoder and decoder path. Our approach helps in obtaining the quintessential components from the feature maps, in addition to the absolute convergence of the model. It is largely assessed on the publicly accessible LIDC‐IDRI dataset. The average Dice Similarity Coefficient (DSC) obtained is 81.10% with an Intersection over Union (IoU/ Jaccard Index) of 72.24%. Exploratory outcomes prove that our architecture achieves ameliorate performance. [ABSTRACT FROM AUTHOR]

It is challenging to screen for and treat pancreatic cancer (PC), an extremely malignant tumor, both early in the course of the disease and as part of the later stages of treatment. In this article, a computer-aided diagnosis (CAD) technique for detecting PC early, particularly when assessing PC staging, is suggested. With this, clinical staff will be better equipped to give a treatment plan and can intervene with therapy early on as a result. This article addresses the process of effectively segmenting and classifying a pancreatic tumor using a deep learning (DL) network by following the four stages outlined below. Initially, computed tomography (CT) images are used for diagnosis, which is obtained from TCIA public access. Following the raw image acquisition, these images need to be pre-processed using the Boosted Anisotropic Diffusion Filter (BADF) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, using the DMFCM segmentation approach, the images are segmented. Through Bag of Visual Words (BOVW) and Support Vector Machine (SVM), features are extracted, and the best features are given as input to the classifier. Eventually, classification is carried out using optimized 3D convoluted neural networks (3D CNN) using Improved Harris Hawks Optimization (IHHO). The implemented model achieved better results of 98.32% accuracy, 99% sensitivity, and 99% specificity. The proposed model is compared to some cutting-edge models such as normal CNNs, LSTMs, RESNETs, RNNs, and 3D CNNs to determine which one performs well. In terms of specificity, sensitivity, accuracy, and recall, the proposed model scored better than other models. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the detection rate and performance of 18F-FDG PET alone (PET), the combination of PET and low-dose thick-slice CT (PET/lCT), PET and diagnostic thin-slice CT (PET/dCT), and additional computer-aided detection (PET/dCT/CAD) for lung nodules (LN)/metastases in tumor patients. Along with this, assessment of inter-reader agreement and time requirement for different techniques were evaluated as well. Methods: In 100 tumor patients (56 male, 44 female; age range: 22–93 years, mean age: 60 years) 18F-FDG PET images, low-dose CT with shallow breathing (5 mm slice thickness), and diagnostic thin-slice CT (1 mm slice thickness) in full inspiration were retrospectively evaluated by three readers with variable experience (junior, mid-level, and senior) for the presence of lung nodules/metastases and additionally analyzed with CAD. Time taken for each analysis and number of the nodules detected were assessed. Sensitivity, specificity, positive and negative predictive value, accuracy, and Receiver operating characteristic (ROC) analysis of each technique was calculated. Histopathology and/or imaging follow-up served as reference standard for the diagnosis of metastases. Results: Three readers, on an average, detected 40 LN in 17 patients with PET only, 121 LN in 37 patients using ICT, 283 LN in 60 patients with dCT, and 282 LN in 53 patients with CAD. On average, CAD detected 49 extra LN, missed by the three readers without CAD, whereas CAD overall missed 53 LN. There was very good inter-reader agreement regarding the diagnosis of metastases for all four techniques (kappa: 0.84–0.93). The average time required for the evaluation of LN in PET, lCT, dCT, and CAD was 25, 31, 60, and 40 s, respectively; the assistance of CAD lead to average 33% reduction in time requirement for evaluation of lung nodules compared to dCT. The time-saving effect was highest in the less experienced reader. Regarding the diagnosis of metastases, sensitivity and specificity combined of all readers were 47.8%/96.2% for PET, 80.0%/81.9% for PET/lCT, 100%/56.7% for PET/dCT, and 95.6%/64.3% for PET/CAD. No significant difference was observed regarding the ROC AUC (area under the curve) between the imaging methods. Conclusion: Implementation of CAD for the detection of lung nodules/metastases in routine 18F-FDG PET/CT read-out is feasible. The combination of diagnostic thin-slice CT and CAD significantly increases the detection rate of lung nodules in tumor patients compared to the standard PET/CT read-out. PET combined with low-dose CT showed the best balance between sensitivity and specificity regarding the diagnosis of metastases per patient. CAD reduces the time required for lung nodule/metastasis detection, especially for less experienced readers. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism (PE) is a common but fatal clinical condition and the gold standard of diagnosis is computed tomography pulmonary angiography (CTPA). Prompt diagnosis and rapid treatment can dramatically reduce mortality in patients. However, the diagnosis of PE is often delayed and missed. Methods: In this study, we identified a deep learning model Scaled‐YOLOv4 that enables end‐to‐end automated detection of PE to help solve these problems. A total of 307 CTPA data (Tianjin 142 cases, Linyi 133 cases, and FUMPE 32 cases) were included in this study. The Tianjin dataset was divided 10 times in the ratio of training set: validation set: test set = 7:2:1 for model tuning, and both the Linyi and FUMPE datasets were used as independent external test sets to evaluate the generalization of the model. Results: Scaled‐YOLOv4 was able to process one patient in average 3.55 s [95% CI: 3.51–3.59 s]. It also achieved an average precision (AP) of 83.04 [95% CI: 79.36–86.72] for PE detection on the Tianjin test set, and 75.86 [95% CI: 75.48–76.24] and 72.74 [95% CI: 72.10–73.38] on Linyi and FUMPE, respectively. Conclusions: This deep learning algorithm helps detect PE in real time, providing radiologists with aided diagnostic evidence without increasing their workload, and can effectively reduce the probability of delayed patient diagnosis. [ABSTRACT FROM AUTHOR]

Background: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. Purpose: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). Material and Methods: Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test Results: With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% (P = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001). Conclusion: CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical deep-fusion learning scheme in a CAD framework for the detection of nodules from computed tomography (CT) scans. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest. This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes raw images. The second model uses a single type of feature image having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. The proposed CAD frameworks are tested using lung CT scans from the LIDC/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification. The proposed hierarchical deep-fusion learning model achieved a sensitivity of 95% with only 0.4 fp/scan. [ABSTRACT FROM AUTHOR]

This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordinary 2-dimensional (2D) model. Methods: We analyzed 696 brain metastases on 127 contrast-enhanced computed tomography (CT) scans from 127 patients with brain metastases. The scans were randomly divided into training (n = 79), validation (n = 18), and test (n = 30) datasets. Single-shot detector (SSD) models with a feature fusion module were constructed, trained, and compared using the lesion-based sensitivity, positive predictive value (PPV), and the number of false positives per patient at a confidence threshold of 50%. Results: The 2.5D SSD model had a significantly higher PPV (t test, p < 0.001) and a significantly smaller number of false positives (t test, p < 0.001). The sensitivities of the 2D and 2.5D models were 88.1% (95% confidence interval [CI], 86.6–89.6%) and 88.7% (95% CI, 87.3–90.1%), respectively. The corresponding PPVs were 39.0% (95% CI, 36.5–41.4%) and 58.9% (95% CI, 55.2–62.7%), respectively. The numbers of false positives per patient were 11.9 (95% CI, 10.7–13.2) and 4.9 (95% CI, 4.2–5.7), respectively. Conclusion: Our results indicate that 2.5D deep-learning, object detection models, which use information about the continuity between adjacent slices, may reduce false positives and improve the performance of automated detection of brain metastases compared with ordinary 2D models. [ABSTRACT FROM AUTHOR]

The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement. This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work. We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy. [ABSTRACT FROM AUTHOR]

Imaging techniques widely use Computed Tomography (CT) scans for various purposes, such as screening, diagnosis, and decision-making. Of all, it holds true for bone injuries. To build fully automated Computer-Aided Detection (CADe) and Diagnosis (CADx) tools and techniques, it requires fairly large amount of data (with gold standard). Therefore, in this paper, since state-of-the-art works relied on small dataset, we introduced a CT image dataset on limbs that is designed to understand bone injuries. Our dataset is a collection of 24 patient-specific CT cases having fractures at upper and lower limbs. From upper limbs, 8 cases were collected from bones in/around the shoulder (left and right). Similarly, from lower limbs, 16 cases were collected from knees (left and right). Altogether, 5684 CT images (upper limbs: 2057 and lower limbs: 3627) were collected. Each patient-specific CT case is composed of maximum 257 scans/slices in average. Of all, clinically approved annotations were made on every 10th slices, resulting in 1787 images. Importantly, no fractured limbs were missed in our annotation. Besides, to avoid privacy and confidential issues, patient-related information were deleted. The proposed dataset could be a promising resource for the medical imaging research community, where imaging techniques are employed for various purposes. To the best of our knowledge, this is the first time 5K+ CT images on fractured limbs are provided for research and educational purposes. [ABSTRACT FROM AUTHOR]

Purpose: Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. Methods: The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder–decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three‐dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non‐nodules. In the public LIDC‐IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross‐validation scheme. The free‐response receiver operating characteristic curve is used for performance assessment. Results: The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection. [ABSTRACT FROM AUTHOR]

Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT). Patients and Methods: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. Results: The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer. Conclusion: The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management. [ABSTRACT FROM AUTHOR]

Lung cancer is becoming the major cause of cancer-related deaths in human worldwide and Saudi Arabia is not an exception. Therein the identification of potentially malignant lung nodules is essential for the diagnosis and clinical management of lung cancer. Unfortunately, in clinical practice, however, interpretation of Computed Tomography (CT) images is challenging for radiologists due to the large number of cases. It is therefore extremely important task to develop computer aided diagnosis (CAD) systems that can aid and enhance the radiologist to potentially reduce false positive (FP) findings. Even though numerous methodologies are proposed for CAD system in the literatures, the one proposed in this work will definitely stand out in improving the sensitivity and specificity for the detection of small nodules particularly in low dose CT images. This work attempts to employ the powerful tool, radiomics quantitative imaging features within curvelet domain to detect and characterize lung nodules with improved sensitivity and specificity. Subsequently, support vector machine (SVM) is used to learn the 2D stochastic and 3D anatomic features of curvelet coefficients and classifies suspected regions either as malignant or benign based on geometric, texture and intensity. A preliminary analysis of the proposed methodology is presented and compared against the metrics, sensitivity, specificity and accuracy on publicly available LIDC database to serve as a benchmark for future research efforts. [ABSTRACT FROM AUTHOR]

Radiotherapy is one of the major option used in cancer management. The treatment involves several steps, one of which is the construction of a computed tomography (CT) model of the patient so that the target tissues and organs at risk (OARs) surrounding that target can be evaluated. With the CT, the responsible physician delimits the OARs slice by slice, as the spinal cord that comprises almost all the tomography becomes more tiring to be segmented and thus susceptible to errors. Thus, this paper presents a method of spinal cord segmentation in planning CT for radiotherapy using template matching, histogram matching and a fully convolutional neural network. The result achieved an accuracy of 99.38%, specificity of 99.12%, sensitivity of 93.83%, and dice index of 81.33%, without any segmentation refinement. [ABSTRACT FROM AUTHOR]

Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect. [ABSTRACT FROM AUTHOR]

Annotating lesion locations by radiologists' manual marking is a key step to provide reference standard for the training and testing of a computer-aided detection system by supervised machine learning. Inter-reader variability is not uncommon in readings even by expert radiologists. This study evaluated the variability of the radiologist-identified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markings from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variability of radiologists was evaluated by analyzing the agreement between two radiologists. For the 40 cases, 475 and 514 PEs were identified by radiologists R1 and R2 in the initial independent readings, respectively. For a total of 545 marks by the two radiologists, 81.5% (444/545) of the marks agreed but 101 marks in 36 cases differed. After consensus, 65 (64.4%) and 36 (35.6%) of the 101 marks were determined to be true PEs and false positives (FPs), respectively. Of these, 48 and 17 were false negatives (FNs) and 14 and 22 were FPs by R1 and R2, respectively. Our study demonstrated that there is substantial variability in reference standards provided by radiologists, which impacts the performance assessment of a lesion detection system. Combination of multiple radiologists' readings and consensus is needed to improve the reliability of a reference standard. [ABSTRACT FROM AUTHOR]

Objectives: To develop a U‐Net–based deep learning approach (U‐DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer‐assisted bladder cancer detection and treatment response assessment pipeline. Materials and methods: A dataset of 173 cases including 81 cases in the training/validation set (42 masses, 21 with wall thickening, 18 normal bladders), and 92 cases in the test set (43 masses, 36 with wall thickening, 13 normal bladders) were used with Institutional Review Board approval. An experienced radiologist provided three‐dimensional (3D) hand outlines for all cases as the reference standard. We previously developed a bladder segmentation method that used a deep learning convolution neural network and level sets (DCNN‐LS) within a user‐input bounding box. However, some cases with poor image quality or with advanced bladder cancer spreading into the neighboring organs caused inaccurate segmentation. We have newly developed an automated U‐DL method to estimate a likelihood map of the bladder in CTU. The U‐DL did not require a user‐input box and the level sets for postprocessing. To identify the best model for this task, we compared the following models: (a) two‐dimensional (2D) U‐DL and 3D U‐DL using 2D CT slices and 3D CT volumes, respectively, as input, (b) U‐DLs using CT images of different resolutions as input, and (c) U‐DLs with and without automated cropping of the bladder as an image preprocessing step. The segmentation accuracy relative to the reference standard was quantified by six measures: average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), average Hausdorff distance (AHD), and the average Jaccard index (AJI). As a baseline, the results from our previous DCNN‐LS method were used. Results: In the test set, the best 2D U‐DL model achieved AVI, AVE, AAVE, AMD, AHD, and AJI values of 93.4 ± 9.5%, −4.2 ± 14.2%, 9.2 ± 11.5%, 2.7 ± 2.5 mm, 9.7 ± 7.6 mm, 85.0 ± 11.3%, respectively, while the corresponding measures by the best 3D U‐DL were 90.6 ± 11.9%, −2.3 ± 21.7%, 11.5 ± 18.5%, 3.1 ± 3.2 mm, 11.4 ± 10.0 mm, and 82.6 ± 14.2%, respectively. For comparison, the corresponding values obtained with the baseline method were 81.9 ± 12.1%, 10.2 ± 16.2%, 14.0 ± 13.0%, 3.6 ± 2.0 mm, 12.8 ± 6.1 mm, and 76.2 ± 11.8%, respectively, for the same test set. The improvement for all measures between the best U‐DL and the DCNN‐LS were statistically significant (P < 0.001). Conclusion: Compared to a previous DCNN‐LS method, which depended on a user‐input bounding box, the U‐DL provided more accurate bladder segmentation and was more automated than the previous approach. [ABSTRACT FROM AUTHOR]

Highlights • This work investigates computational method for automatic detection of spinal cord in planning CT scan for radiotherapy. • The spinal cord was detected in CT scan using adaptive template matching, IMSLIC and convolutional neural network. • The method was applied with 36 CT scans with an average of 200 slices. • The method uses image processing and deep learning techniques. • The method achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images. Abstract Background and Objective The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images. Methods The techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification. Results The methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord. Conclusions It is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions. [ABSTRACT FROM AUTHOR]

Computer-aided detection (CAD) systems can assist radiologists in reducing the interpretation time and improving the detection results in computed tomographic colonography (CTC). However, existing false positives (FPs) impair the advantages of CAD systems. This study aims to develop new morphological features for the FP reduction while maintaining high detection sensitivity. Volumetric feature maps are computed for each polyp candidate by using three-dimensional (3-D) geodesic distance transformation, circular transformation (CcT), and quantized convergence index (QCI) filters. Then, new morphological features are developed based on the curvature, fractal dimension, and volumetric feature maps. To the best of our knowledge, we are also the first to develop 3-D CcT and QCI filters specifically for colonic polyps. The new morphological features were evaluated to reduce the FPs by using 456 oral contrast-enhanced CT scans from 228 patients with 130 polyps ≥5 mm. For comparison, the well-defined features from our previous work were used to generate a baseline reference. The additional use of the new morphological features reduced the FP rate from 4.2 to 2.0 FPs per scan (i.e., 52.4% FP reduction percentage) at 96.2% by-polyp sensitivity and from 4.5 to 2.1 FPs per scan (i.e., 53.3% FP reduction percentage) at 93.9% per-scan sensitivity for polyps ≥5 mm. Experimental results indicate that the new morphological features can effectively reduce the FP rate without sacrificing detection sensitivity. We believe that the newly developed morphological features would advance the CAD systems to assist radiologists in interpreting CTC images. [ABSTRACT FROM AUTHOR]

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures. [ABSTRACT FROM AUTHOR]

Purpose: To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D).Materials and Methods: Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ± standard deviation: 63.1 ± 11.0 years) and 19 females (mean age, 65.1 ± 12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test.Results: Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001).Conclusion: The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as “solitary white nodule-like blob.” However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed. We employed stationary wavelet transform and convergence index filter to extract the texture features and used AdaBoost to generate white nodule-likeness map. A solitary feature was defined to evaluate the isolation degree of candidates. Both the isolation degree and the white nodule likeness were used as final evaluation of lung nodule candidates. The proposed method shows better performance and robustness than those reported in previous research. More than 80% and 93% of lung nodules in the lung field in the Japanese Society of Radiological Technology (JSRT) database were detected when the false positives per image were two and five, respectively. The proposed approach has the potential of being used in clinical practice. [ABSTRACT FROM PUBLISHER]

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems. [ABSTRACT FROM AUTHOR]

Background: Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules. Methods: In this paper, a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm segments two lung lobes to increase processing speed and accuracy. Second, template-matching is applied to detect the suspicious nodule candidates, including both nodules and some blood vessels. Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical features in three steps. In these steps, the size, long and short diameters and sphericity are used to decrease the false-positive rate. Results: In the first step, some vessels that are parallel to CT cross-plane are identified. In the second step, oblique vessels are detected using shift of center of gravity in two successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps, vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in this work. Conclusions: Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable in comparison to other related works. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure. [ABSTRACT FROM AUTHOR]

Background: In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. Purpose: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials. Material and Methods: A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline. Results: The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). Conclusion: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials. [ABSTRACT FROM AUTHOR]

The present study aims to quantitatively measure adiposity-related image features and to test the feasibility of applying multivariate statistical data analysis-based prediction models to generate a novel clinical marker and predict the benefit of epithelial ovarian cancer (EOC) patients with and without maintenance bevacizumab-based chemotherapy. A dataset involving computed tomography (CT) images acquired from 59 patients diagnosed with advanced EOC was retrospectively collected. Among them, 32 patients received maintenance bevacizumab following primary chemotherapy, while 27 did not. A computer-aided detection scheme was developed to automatically segment visceral and subcutaneous fat areas depicted on CT images of abdominal sections, and 7 adiposity-related image features were computed. Upon combining these features with the measured body mass index, multivariate data analyses were performed using three statistical models (multiple linear, logistic and Cox proportional hazards regressions) to analyze the association between the model-generated prediction results and the treatment outcome, including progression-free survival (PFS) and overall survival (OS) of the patients. The results demonstrated that applying all three prediction models yielded a significant association between the adiposity-related image features and patients' PFS or OS in the group of the patients who received maintenance bevacizumab (P<0.010), while there was no significant difference when these prediction models were applied to predict both PFS and OS in the group of patients that did not receive maintenance bevacizumab. Therefore, the present study demonstrated that the use of a quantitative adiposity-related image feature-based statistical model may generate a novel clinical marker to predict who will benefit among EOC patients receiving maintenance bevacizumab-based chemotherapy. [ABSTRACT FROM AUTHOR]

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using an active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors' ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initially computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716±0.071 and 0.642±0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859±0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy. [ABSTRACT FROM AUTHOR]

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data. [ABSTRACT FROM AUTHOR]

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160?000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of radiation dose reduction and iterative reconstruction (IR) on the performance of computer-aided detection (CAD) for pulmonary nodules.Methods: In this prospective study twenty-five patients were included who were scanned for pulmonary nodule follow-up. Image acquisition was performed at routine dose and three reduced dose levels in a single session by decreasing mAs-values with 45%, 60% and 75%. Tube voltage was fixed at 120 kVp for patients ≥ 80 kg and 100 kVp for patients < 80 kg. Data were reconstructed with filtered back projection (FBP), iDose(4) (levels 1,4,6) and IMR (levels 1-3). All noncalcified solid pulmonary nodules ≥ 4 mm identified by two radiologists in consensus served as the reference standard. Subsequently, nodule volume was measured with CAD software and compared to the reference consensus. The numbers of true-positives, false-positives and missed pulmonary nodules were evaluated as well as the sensitivity.Results: Median effective radiation dose was 2.2 mSv at routine dose and 1.2, 0.9 and 0.6 mSv at respectively 45%, 60% and 75% reduced dose. A total of 28 pulmonary nodules were included. With FBP at routine dose, 89% (25/28) of the nodules were correctly identified by CAD. This was similar at reduced dose levels with FBP, iDose(4) and IMR. CAD resulted in a median number of false-positives findings of 11 per scan with FBP at routine dose (93% of the CAD marks) increasing to 15 per scan with iDose(4) (95% of the CAD marks) and 26 per scan (96% of the CAD marks) with IMR at the lowest dose level.Conclusion: CAD can identify pulmonary nodules at submillisievert dose levels with FBP, hybrid and model-based IR. However, the number of false-positive findings increased using hybrid and especially model-based IR at submillisievert dose while dose reduction did not affect the number of false-positives with FBP. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing a computer-aided detection system for bladder cancer on CT urography (CTU). In this study, the authors focused on developing a system for detecting masses fully or partially within the contrast-enhanced (C) region of the bladder. Methods: With IRB approval, a data set of 70 patients with biopsy-proven bladder lesions fully or partially immersed within the contrast-enhanced region (C region) of the bladder was collected for this study: 35 patients for the training set (39 malignant, 7 benign lesions) and 35 patients for the test set (49 malignant, 4 benign lesions). The bladder in the CTU images was automatically segmented using the authors' conjoint level set analysis and segmentation system, which they developed specifically to segment the bladder. A closed contour of the C region of the bladder was generated by maximum intensity projection using the property that the dependently layering contrast material in the bladder will be filled consistently to the same level along all CTU slices due to gravity. Potential lesion candidates within the C region contour were found using the authors' Straightened Periphery ANalysis (SPAN) method. SPAN transforms a bladder wall to a straightened thickness profile, marks suspicious pixels on the profile, and clusters them into regions of interest to identify potential lesion candidates. The candidate regions were automatically segmented using the authors' autoinitialized cascaded level set segmentation method. Twenty-three morphological features were automatically extracted from the segmented lesions. The training set was used to determine the best subset of these features using simplex optimization with the leave-one-out case method. A linear discriminant classifier was designed for the classification of bladder lesions and false positives. The detection performance was evaluated on the independent test set by free-response receiver operating characteristic analysis. Results: At the prescreening step, the authors' system achieved 84.4% sensitivity with an average of 4.3 false positives per case (FPs/case) for the training set, and 84.9% sensitivity with 5.4 FPs/case for the test set. After linear discriminant analysis (LDA) classification with the selected features, the FP rate improved to 2.5 FPs/case for the training set, and 4.3 FPs/case for the test set without missing additional true lesions. By varying the threshold for the LDA scores, at 2.5 FPs/case, the sensitivities were 84.4% and 81.1% for the training and test sets, respectively. At 1.7 FPs/case, the sensitivities decreased to 77.8% and 75.5%, respectively. Conclusions: The results demonstrate the feasibility of the authors' method for detection of bladder lesions fully or partially immersed in the contrast-enhanced region of CTU. [ABSTRACT FROM AUTHOR]

We present a novel descriptor for the characterization of pulmonary nodules in computed tomography (CT) images. The descriptor encodes information on nodule morphology and has scale-invariant and rotation-invariant properties. Information on nodule morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a spectrum. A library of spectra is created and labeled via unsupervised clustering, obtaining a Bag-of-Frequencies, which is used to assign each spectra a label. The descriptor is obtained as the histogram of labels along all the spheres. Additional contributions are a technique to estimate the nodule size, based on the sampling strategy, as well as a technique to choose the most informative plane to cut a 2-D view of the nodule in the 3-D image. We evaluate the descriptor on several nodule morphology classification problems, namely discrimination of nodules versus vascular structures and characterization of spiculation. We validate the descriptor on data from European screening trials NELSON and DLCST and we compare it with state-of-the-art approaches for 3-D shape description in medical imaging and computer vision, namely SPHARM and 3-D SIFT, outperforming them in all the considered experiments. [ABSTRACT FROM PUBLISHER]

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e - 3) on all calculi from 1 to 433 mm³ in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. [ABSTRACT FROM AUTHOR]

The article presents a 2014 study that deals with the performance of current computer-aided schemes for the detection of lung lesions. Topics include computer-aided detection, lung computed tomography imaging, higher-order tensor, Gaussian kernel, structure support vector machine, and active appearance model for segmentation of lung fields.

Lung cancer stands out among all other types of cancer for presenting one of the highest incidence rates and one of the highest rates of mortality. Unfortunately, this disease is often diagnosed late, affecting the treatment result. One of the hopes for changing this scenario lies in achieving a more precocious diagnosis of lung cancer through low-dose computed tomography, used as a screening method in risk groups of smokers or former smokers with elevated tobacco load. In order to help specialists in this search and identification of lung nodules in tomographic images, many research centers develop computer-aided detection systems (CAD systems) which are intended to automate procedures. This work has the purpose of developing a methodology for automatic detection of small lung nodules (with sizes between 2 and 10 mm) through image processing and pattern recognition techniques. Some of these techniques are widely used in similar applications, as is the case of the region growing technique for segmentation of the pulmonary parenchyma. Other techniques, with more restricted application, are the Gaussian mixture models and the Hessian matrix for segmentation of structures inside the lung, Tsallis׳s and Shannon׳s entropy measurements as texture descriptors, and support vector machine to classify suspect regions as either nodules or non-nodules. The results achieved with the use of this set of techniques, applied to a sample with 28 exams from a public database, showed that small nodules were detected with a sensitivity of 90.6%, a specificity of 85% and an accuracy of 88.4%. The rate of false positives per exam was of 1.17. [ABSTRACT FROM AUTHOR]

Purpose: The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors' coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors' multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors' patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases. Conclusions: The authors' MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels. [ABSTRACT FROM AUTHOR]