Deep Learning-based Segmentation Methods Research Articles

Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation.

Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.

Asymmetric multi-task attention network for prostate bed segmentation in computed tomography images.

Post-prostatectomy radiotherapy requires accurate annotation of the prostate bed (PB), i.e., the residual tissue after the operative removal of the prostate gland, to minimize side effects on surrounding organs-at-risk (OARs). However, PB segmentation in computed tomography (CT) images is a challenging task, even for experienced physicians. This is because PB is almost a "virtual" target with non-contrast boundaries and highly variable shapes depending on neighboring OARs. In this work, we propose an asymmetric multi-task attention network (AMTA-Net) for the concurrent segmentation of PB and surrounding OARs. Our AMTA-Net mimics experts in delineating the non-contrast PB by explicitly leveraging its critical dependency on the neighboring OARs (i.e., the bladder and rectum), which are relatively easy to distinguish in CT images. Specifically, we first adopt a U-Net as the backbone network for the low-level (or prerequisite) task of the OAR segmentation. Then, we build an attention sub-network upon the backbone U-Net with a series of cascaded attention modules, which can hierarchically transfer the OAR features and adaptively learn discriminative representations for the high-level (or primary) task of the PB segmentation. We comprehensively evaluate the proposed AMTA-Net on a clinical dataset composed of 186 CT images. According to the experimental results, our AMTA-Net significantly outperforms current clinical state-of-the-arts (i.e., atlas-based segmentation methods), indicating the value of our method in reducing time and labor in the clinical workflow. Our AMTA-Net also presents better performance than the technical state-of-the-arts (i.e., the deep learning-based segmentation methods), especially for the most indistinguishable and clinically critical part of the PB boundaries. Source code is released at https://github.com/superxuang/amta-net.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-based method for male pelvic multi-organ segmentation on transrectal ultrasound images. We developed an anchor-free mask convolutional neural network (CNN) that consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS), and a mask head. The backbone extracts multi-level and multi-scale features from an ultrasound (US) image. The FOCS utilizes these features to detect and label (classify) the volume-of-interests (VOIs) of organs. In contrast to the design of a previously investigated mask regional CNN (Mask R-CNN), the FCOS is anchor-free, which can capture the spatial correlation of multiple organs. The mask head performs segmentation on each detected VOI, where a spatial attention strategy is integrated into the mask head to focus on informative feature elements and suppress noise. For evaluation, we retrospectively investigated 83 prostate cancer patients by fivefold cross-validation and a hold-out test. The prostate, bladder, rectum, and urethra were segmented and compared with manual contours using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95 ), mean surface distance (MSD), center of mass distance (CMD), and volume difference (VD). The proposed method visually outperforms two competing methods, showing better agreement with manual contours and fewer misidentified speckles. In the cross-validation study, the respective DSC and HD95 results were as follows for each organ: bladder 0.75±0.12, 2.58±0.7mm; prostate 0.93±0.03, 2.28±0.64mm; rectum 0.90±0.07, 1.65±0.52mm; and urethra 0.86±0.07, 1.85±1.71mm. For the hold-out tests, the DSC and HD95 results were as follows: bladder 0.76±0.13, 2.93±1.29mm; prostate 0.94±0.03, 2.27±0.79mm; rectum 0.92±0.03, 1.90±0.28mm; and urethra 0.85±0.06, 1.81±0.72mm. Segmentation was performed in under 5seconds. The proposed method demonstrated fast and accurate multi-organ segmentation performance. It can expedite the contouring step of prostate brachytherapy and potentially enable auto-planning and auto-evaluation.

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model

Background and Objective: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel segmentation in eye fundus images.Methods: The approach consists of a convolutional neural network based on a simplified version of the U-Net architecture that combines residual blocks and batch normalization in the up- and downscaling phases. The network receives patches extracted from the original image as input and is trained with a novel loss function that considers the distance of each pixel to the vascular tree. At its output, it generates the probability of each pixel of the input patch belonging to the vascular structure. The application of the network to the patches in which a retinal image can be divided allows obtaining the pixel-wise probability map of the complete image. This probability map is then binarized with a certain threshold to generate the blood vessel segmentation provided by the method.Results: The method has been developed and evaluated in the DRIVE, STARE and CHASE_Db1 databases, which offer a manual segmentation of the vascular tree by each of its images. Using this set of images as ground truth, the accuracy of the vessel segmentations obtained for an operating point proposal (established by a single threshold value for each database) was quantified. The overall performance was measured using the area of its receiver operating characteristic curve. The method demonstrated robustness in the face of the variability of the fundus images of diverse origin, being capable of working with the highest level of accuracy in the entire set of possible points of operation, compared to those provided by the most accurate methods found in literature.Conclusions: The analysis of results concludes that the proposed method reaches better performance than the rest of state-of-art methods and can be considered the most promising for integration into a real tool for vascular structure segmentation.

Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery

ABSTRACT Deep learning-based semantic segmentation methods, such as fully convolutional networks (FCNs), are state-of-the-art techniques for object extraction from high spatial resolution images. However, collecting massive scene-formed training samples typically required in FCNs is time-consuming and labour-intensive. A suit of automatic sample augmentation schemes based on simulated scene generation is proposed in this study to reduce the manual workload. Proposed schemes include style transfer, target embedding, and mixed modes by utilizing techniques, such as texture transfer, image inpainting, and region-line primitive association framework, which automatically expand the sample set on the basis of a small number of real samples. Dock extraction experiments using UNet are conducted on China’s GaoFen-2 imagery with expanded sample sets. Results showed that the proposed schemes can successfully generate sufficient simulation samples, increase sample diversity, and subsequently improve semantic segmentation accuracy. Compared with the results that use the original real sample set, measures of F 1-score (F 1) and intersection over union (IoU) of dock extraction accuracy demonstrate a maximum improvement of 20.53% and 23.01%, respectively, after sample augmentation.

Loss odyssey in medical image segmentation.

The loss function is an important component in deep learning-based segmentation methods. Over the past five years, many loss functions have been proposed for various segmentation tasks. However, a systematic study of the utility of these loss functions is missing. In this paper, we present a comprehensive review of segmentation loss functions in an organized manner. We also conduct the first large-scale analysis of 20 general loss functions on four typical 3D segmentation tasks involving six public datasets from 10+ medical centers. The results show that none of the losses can consistently achieve the best performance on the four segmentation tasks, but compound loss functions (e.g. Dice with TopK loss, focal loss, Hausdorff distance loss, and boundary loss) are the most robust losses. Our code and segmentation results are publicly available and can serve as a loss function benchmark. We hope this work will also provide insights on new loss function development for the community.

Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment.

Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling.

Accurate segmentation of the prostate on computed tomography (CT) has many diagnostic and therapeutic applications. However, manual segmentation is time-consuming and suffers from high inter- and intra-observer variability. Computer-assisted approaches are useful to speed up the process and increase the reproducibility of the segmentation. Deep learning-based segmentation methods have shown potential for quick and accurate segmentation of the prostate on CT images. However, difficulties in obtaining manual, expert segmentations on a large quantity of images limit further progress. Thus, we proposed an approach to train a base model on a small, manually-labeled dataset and fine-tuned the model using unannotated images from a large dataset without any manual segmentation. The datasets used for pre-training and fine-tuning the base model have been acquired in different centers with different CT scanners and imaging parameters. Our fine-tuning method increased the validation and testing Dice scores. A paired, two-tailed t-test shows a significant change in test score (p = 0.017) demonstrating that unannotated images can be used to increase the performance of automated segmentation models.

MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images.

Stroke is a serious manifestation of various cerebrovascular diseases and one of the most dangerous diseases in the world today. Volume quantification and location detection of chronic stroke lesions provide vital biomarkers for stroke rehabilitation. Recently, deep learning has seen a rapid growth, with a great potential in segmenting medical images. In this work, unlike most deep learning-based segmentation methods utilizing only magnetic resonance (MR) images as the input, we propose and validate a novel stroke lesion segmentation approach named multi-inputs UNet (MI-UNet) that incorporates brain parcellation information, including gray matter (GM), white matter (WM) and lateral ventricle (LV). The brain parcellation is obtained from 3D diffeomorphic registration and is concatenated with the original MR image to form two-channel inputs to the subsequent MI-UNet. Effectiveness of the proposed pipeline is validated using a dataset consisting of 229 T1-weighted MR images. Experiments are conducted via a five-fold cross-validation. The proposed MI-UNet performed significantly better than UNet in both 2D and 3D settings. Our best results obtained by 3D MI-UNet has superior segmentation performance, as measured by the Dice score, Hausdorff distance, average symmetric surface distance, as well as precision, over other state-of-the-art methods.

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework.

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of and an intersection-over-union (IoU) score of —higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were better than those of the popular biomedical segmentation method U-Net.

Confidence-Guided Topology-Preserving Layer Segmentation for Optical Coherence Tomography Images With Focus-Column Module

Optical coherence tomography (OCT) imaging widely used in retinal examinations yields high-resolution cross-sectional scans of the retina. As a key indicator for studying the development of retinopathy, the change of layer thickness needs to be accurately measured. Although many deep learning-based segmentation methods have been developed, most of them do not explicitly consider the strict order of the retina layers, which easily lead to topological errors. In this article, we propose a novel segmentation framework that employs the distance maps of layer surfaces to convert the segmentation task into multitasking problem for classification and regression, and obtains the topology-guaranteed result through a fusion module. In addition, to cope with the different difficulty in retinal layer segmentation, a confidence network is introduced as a hard-or-easy recognizer to provide online training guidance for the segmentation network. The proposed method is investigated on one OCT data set with slight deformation and two data sets with severe deformation, and the experiment results demonstrate that the proposed method is more effective than other state-of-the-art methods for layer segmentation on OCT B-scans.

Cellpose: a generalist algorithm for cellular segmentation.

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN.

Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer-aided diagnosis (CAD). This work aims to develop a deep learning-based method for breast tumor segmentation using three-dimensional (3D) ABUS automatically. For breast tumor segmentation in ABUS, we developed a Mask scoring region-based convolutional neural network (R-CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R-CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross-validation and 30 were used for hold-out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland-Altman analysis. The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross-validation and hold-out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646±1.191 and 1.665±1.129mm, 0.489±0.406 and 0.475±0.371mm, 0.755±0.755 and 0.751±0.508mm, and 0.672±0.612 and 0.665±0.729mm. The mean volumetric difference (mean and±1.96 standard deviation) was 0.47cc ([-0.77, 1.71)) for the cross-validation and 0.23cc ([-0.23 0.69]) for hold-out test, respectively. We developed a novel Mask scoring R-CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning-based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study.

To develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD). We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests. The automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36mm for pericardial fat and 1.00mm for myocardium. The proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study. • Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

Fast body part segmentation and tracking of neonatal video data using deep learning

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates’ body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications.Graphical This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.

Deep pyramid local attention neural network for cardiac structure segmentation in two-dimensional echocardiography.

Automatic semantic segmentation in 2D echocardiography is vital in clinical practice for assessing various cardiac functions and improving the diagnosis of cardiac diseases. However, two distinct problems have persisted in automatic segmentation in 2D echocardiography, namely the lack of an effective feature enhancement approach for contextual feature capture and lack of label coherence in category prediction for individual pixels. Therefore, in this study, we propose a deep learning model, called deep pyramid local attention neural network (PLANet), to improve the segmentation performance of automatic methods in 2D echocardiography. Specifically, we propose a pyramid local attention module to enhance features by capturing supporting information within compact and sparse neighboring contexts. We also propose a label coherence learning mechanism to promote prediction consistency for pixels and their neighbors by guiding the learning with explicit supervision signals. The proposed PLANet was extensively evaluated on the dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS) and sub-EchoNet-Dynamic, which are two large-scale and public 2D echocardiography datasets. The experimental results show that PLANet performs better than traditional and deep learning-based segmentation methods on geometrical and clinical metrics. Moreover, PLANet can complete the segmentation of heart structures in 2D echocardiography in real time, indicating a potential to assist cardiologists accurately and efficiently.

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.