Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integrating contextual relationships substantially enhances the representational ability of neural networks. Conventionally, Long Short-Term Memory (LSTM) and Self-Attention (SA) mechanisms have been recognized for their proficiency in capturing global dependencies within data. However, these mechanisms have typically been viewed as distinct modules without a direct linkage. This paper presents the integration of LSTM design with SA sparse coding as a key innovation. It uses linear combinations of LSTM states for SA's query, key, and value (QKV) matrices to leverage LSTM's capability for state compression and historical data retention. This approach aims to rectify the shortcomings of conventional sparse coding methods that overlook temporal information, thereby enhancing SA's ability to do sparse coding and capture global dependencies. Building upon this premise, we introduce two innovative modules that weave the SA matrix into the LSTM state design in distinct manners, enabling LSTM to more adeptly model global dependencies and meld seamlessly with SA without accruing extra computational demands. Both modules are separately embedded into the U-shaped convolutional neural network architecture for handling both 2D and 3D medical images. Experimental evaluations on downstream medical image segmentation tasks reveal that our proposed modules not only excel on four extensively utilized datasets across various baselines but also enhance prediction accuracy, even on baselines that have already incorporated contextual modules. Code is available at https://github.com/yeshunlong/SALSTM.

Diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in hospitalized children: a retrospective study.

BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), influenza A, and respiratory syncytial virus (RSV) infections have similar modes of transmission and clinical symptoms. There is a need to identify simple diagnostic indicators to distinguish these three infections, particularly for community hospitals and low- and middle-income countries that lack nucleic acid detection kits. This study used clinical data to assess the diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in children. METHODS: A total of 1420 children treated at the Hangzhou Children's Hospital between December 2022 and June 2023 were enrolled in this study, of whom 351 had SARS-CoV-2, 671 had influenza, and 398 had RSV. In addition, 243 healthy children were also collected. The blood test results of SARS-CoV-2 patients were compared to those of patients with influenza A and RSV and the healthy controls. The area under the receiver operating characteristic curve (AUC-ROC) was employed to evaluate each blood parameter's diagnostic value. RESULTS: Children with SARS-CoV-2 exhibited notably elevated levels of white blood cell (WBC) count, platelet (PLT) count, neutrophil count, and neutrophil-to-lymphocyte ratio (NLR) compared to influenza A patients (P < 0.05). In contrast, SARS-CoV-2 patients exhibited a decrease in the mean platelet volume to platelet count ratio (MPV/PLT) and the lymphocyte-to-monocyte ratio (LMR) when compared to other individuals (P < 0.05). These parameters had an AUC between 0.5 and 0.7. Compared to patients with RSV, SARS-CoV-2 patients had significantly higher MPV/PLT and significantly lower WBC, lymphocyte, PLT, LMR, and lymphocyte multiplied by platelet (LYM*PLT) values (P < 0.05). However, only LYM*PLT had an acceptable diagnostic value above 0.7 for all age groups. Compared to healthy children, children with COVID-19 exhibited elevated NLR and MPV/PLT levels, alongside decreased lymphocyte, PLT, LMR, and LYM*PLT values. (P < 0.05). The AUC of the LMR, LYM*PLT, and PLT were above 0.7 in all age groups, indicating promising diagnostic values. CONCLUSIONS: The routine blood parameters among patients with COVID-19, influenza A, and RSV differ significantly early in the disease and could be used by clinicians to discriminate between the 3 types of infection.

Rutaecarpine Protects Against Cigarette Smoke-Induced Chronic Obstructive Pulmonary Disease (COPD) in Rats.

Chronic obstructive pulmonary disease (COPD) is a chronic lung inflammatory disease that causes restricted airflow and breathing difficulties. In this work, we attempted to explore the salutary effects of rutaecarpine on COPD-induced rats. Healthy Wistar rats were employed in this study and exposed to cigarette smoke to initiate COPD. The rutaecarpine was given to the rats at 20 and 30 mg/kg dosages, respectively, for 12 weeks. Body weight gain, food uptake, and food efficiency were assessed after treatment completion. The grip strength test was performed to assess muscle strength. The C-reactive protein (CRP), leptin, inflammatory cytokines, and oxidative stress markers were assessed using the corresponding assay kits. The inflammatory cells on the bronchoalveolar lavage fluid (BALF) were counted using Wright-Giemsa staining. The respiratory functions of the experimental rats were measured. The histopathological analysis was done on the lung tissues. The rutaecarpine treatment effectively increased body weight gain, food uptake, and food efficiency in the COPD rats. The levels of leptin were increased, and CRP was reduced by the rutaecarpine. The rutaecarpine regulated the respiratory functions and reduced the inflammatory cell counts and pro-inflammatory markers in the COPD rats. The levels of antioxidants were increased by the rutaecarpine treatment in the COPD rats. The findings of the lung histopathological study also demonstrated the therapeutic effects of rutaecarpine. Overall, the findings of the current study witness the salutary role of rutaecarpine against cigarette smoke-induced COPD in rats. Therefore, it was clear that rutaecarpine could be a promising salutary candidate to treat COPD.

Controllable editing via diffusion inversion on ultra-widefield fluorescein angiography for the comprehensive analysis of diabetic retinopathy.

By incorporating multiple indicators that facilitate clinical decision making and effective management of diabetic retinopathy (DR), a comprehensive understanding of the progression of the disease can be achieved. However, the diversity of DR complications poses challenges to the automatic analysis of various information within images. This study aims to establish a deep learning system designed to examine various metrics linked to DR in ultra-widefield fluorescein angiography (UWFA) images. We have developed a unified model based on image generation that transforms input images into corresponding disease-free versions. By incorporating an image-level supervised training process, the model significantly reduces the need for extensive manual involvement in clinical applications. Furthermore, compared to other comparative methods, the quality of our generated images is significantly superior.

OCTA-500: A retinal dataset for optical coherence tomography angiography study.

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.

Corrections to "Image Projection Network: 3D to 2D Image Segmentation in OCTA Images".

In the above article [1], there is an error in (3). Instead of [Formula: see text] It should be [Formula: see text].

Immune checkpoint inhibitor-related chronic pneumonitis: a case report and literature review.

Immune checkpoint inhibitor (ICI)-related chronic pneumonitis is rare. Limited information is available on the characteristics of this condition. Herein, we present the case of a 54-year-old man with recurrent severe ICI-related pneumonitis. The patient developed fever and dyspnea during both episodes of pneumonitis. He had been previously diagnosed with gastric signet ring cell carcinoma and was undergoing treatment with an anti-PD-1 combination chemotherapy regimen. We reviewed previous case reports of ICI-related pneumonitis according to the primary cancer, time of onset in relation to ICI therapy and chest imaging findings. ICI-related pneumonitis can progress to chronic pneumonitis. Repeated computed tomography imaging showing lung changes in the same location may help to make the diagnosis.

Immune checkpoint inhibitors (ICIs) are a type of medicine that helps fight stomach cancer but sometimes they can cause problems with the lungs. This case report is about a man who had two bad lung incidents after taking ICI medicine. He had trouble breathing and fever both times. Other people have had similar problems with their lungs after being given ICI treatment. We compared chest pictures of the patient receiving ICI treatment over time and saw changes in the same spot meaning there might be a long-term problem with the lungs. We need to do more research to figure out how to treat this problem better.

LAGAN: Lesion-Aware Generative Adversarial Networks for Edema Area Segmentation in SD-OCT Images.

Large volume of labeled data is a cornerstone for deep learning (DL) based segmentation methods. Medical images require domain experts to annotate, and full segmentation annotations of large volumes of medical data are difficult, if not impossible, to acquire in practice. Compared with full annotations, image-level labels are multiple orders of magnitude faster and easier to obtain. Image-level labels contain rich information that correlates with the underlying segmentation tasks and should be utilized in modeling segmentation problems. In this article, we aim to build a robust DL-based lesion segmentation model using only image-level labels (normal v.s. abnormal). Our method consists of three main steps: (1) training an image classifier with image-level labels; (2) utilizing a model visualization tool to generate an object heat map for each training sample according to the trained classifier; (3) based on the generated heat maps (as pseudo-annotations) and an adversarial learning framework, we construct and train an image generator for Edema Area Segmentation (EAS). We name the proposed method Lesion-Aware Generative Adversarial Networks (LAGAN) as it combines the merits of supervised learning (being lesion-aware) and adversarial training (for image generation). Additional technical treatments, such as the design of a multi-scale patch-based discriminator, further enhance the effectiveness of our proposed method. We validate the superior performance of LAGAN via comprehensive experiments on two publicly available datasets (i.e., AI Challenger and RETOUCH).

Viral Metagenomics Reveals Diverse Viruses in Tissue Samples of Diseased Pigs.

The swine industry plays an essential role in agricultural production in China. Diseases, especially viral diseases, affect the development of the pig industry and threaten human health. However, at present, the tissue virome of diseased pigs has rarely been studied. Using the unbiased viral metagenomic approach, we investigated the tissue virome in sick pigs (respiratory symptoms, reproductive disorders, high fever, diarrhea, weight loss, acute death and neurological symptoms) collected from farms of Anhui, Jiangsu and Sichuan Province, China. The eukaryotic viruses identified belonged to the families Anelloviridae, Arteriviridae, Astroviridae, Flaviviridae, Circoviridae and Parvoviridae; prokaryotic virus families including Siphoviridae, Myoviridae and Podoviridae occupied a large proportion in some samples. This study provides valuable information for understanding the tissue virome in sick pigs and for the monitoring, preventing, and treating of viral diseases in pigs.

LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images.

Choroidal neovascularization (CNV) volume prediction has an important clinical significance to predict the therapeutic effect and schedule the follow-up. In this paper, we propose a Lesion Attention Maps-Guided Network (LamNet) to automatically predict the CNV volume of next follow-up visit after therapy based on 3-dimentional spectral-domain optical coherence tomography (SD-OCT) images. In particular, the backbone of LamNet is a 3D convolutional neural network (3D-CNN). In order to guide the network to focus on the local CNV lesion regions, we use CNV attention maps generated by an attention map generator to produce the multi-scale local context features. Then, the multi-scale of both local and global feature maps are fused to achieve the high-precision CNV volume prediction. In addition, we also design a synergistic multi-task predictor, in which a trend-consistent loss ensures that the change trend of the predicted CNV volume is consistent with the real change trend of the CNV volume. The experiments include a total of 541 SD-OCT cubes from 68 patients with two types of CNV captured by two different SD-OCT devices. The results demonstrate that LamNet can provide the reliable and accurate CNV volume prediction, which would further assist the clinical diagnosis and design the treatment options.

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. Considering that manual diagnosis and analysis of medical images require a large number of qualified experts, deep learning can effectively diagnosis and locate critical biomarkers. In this paper, we present a novel model by jointly optimize the cycle generative adversarial network (CycleGAN) and the convolutional neural network (CNN) to detect retinal diseases and localize lesion areas with limited training data. The CycleGAN with cycle consistency can generate more realistic and reliable images. The discriminator and the generator achieve a local optimal solution in an adversarial manner, and the generator and the classifier are in a cooperative manner to distinguish the domain of input images. A novel res-guided sampling block is proposed by combining learnable residual features and pixel-adaptive convolutions. A res-guided U-Net is constructed as the generator by substituting the traditional convolution with the res-guided sampling blocks. Our model achieve superior classification and localization performance on LAG, Ichallenge-PM and Ichallenge-AMD datasets. With clear localization for lesion areas, the competitive results reveal great potentials of the joint optimization network. The source code is available at https://github.com/jizexuan/JointOptmization.

Automated Grading of Diabetic Retinopathy with Ultra-Widefield Fluorescein Angiography and Deep Learning.

PURPOSE: The objective of this study was to establish diagnostic technology to automatically grade the severity of diabetic retinopathy (DR) according to the ischemic index and leakage index with ultra-widefield fluorescein angiography (UWFA) and the Early Treatment Diabetic Retinopathy Study (ETDRS) 7-standard field (7-SF). METHODS: This is a cross-sectional study. UWFA samples from 280 diabetic patients and 119 normal patients were used to train and test an artificial intelligence model to differentiate PDR and NPDR based on the ischemic index and leakage index with UWFA. A panel of retinal specialists determined the ground truth for our data set before experimentation. A confusion matrix as a metric was used to measure the precision of our algorithm, and a simple linear regression function was implemented to explore the discrimination of indexes on the DR grades. In addition, the model was tested with simulated 7-SF. RESULTS: The model classification of DR in the original UWFA images achieved 88.50% accuracy and 73.68% accuracy in the simulated 7-SF images. A simple linear regression function demonstrated that there is a significant relationship between the ischemic index and leakage index and the severity of DR. These two thresholds were set to classify the grade of DR, which achieved 76.8% accuracy. CONCLUSIONS: The optimization of the cycle generative adversarial network (CycleGAN) and convolutional neural network (CNN) model classifier achieved DR grading based on the ischemic index and leakage index with UWFA and simulated 7-SF and provided accurate inference results. The classification accuracy with UWFA is slightly higher than that of simulated 7-SF.

Effect of different levels of stroke volume variation on the endothelial glycocalyx of patients undergoing colorectal surgery: A randomized clinical trial.

NEW FINDINGS: What is the central question of this study? Massive infusion can destroy the endothelial glycocalyx. We compared the serum concentrations of endothelial glycocalyx components and atrial natriuretic peptide and the outcomes of patients with different levels of stroke volume variation (SVV). What is the main finding and its importance? With a decrease in SVV, the serum concentrations of endothelial glycocalyx components and atrial natriuretic peptide increased, whereas the oxygenation index decreased. When the intraoperative SVV was maintained at 7-10%, the patients had better postoperative recovery and shorter postoperative hospital stays. Therefore, it is advisable to maintain the SVV between 7 and 10%. ABSTRACT: Dynamic haemodynamic parameters, such as stroke volume variation (SVV), can be used for blood volume monitoring. However, studies have determined the SVV threshold but not the optimal level. The endothelial glycocalyx (EG) plays an important role in maintaining vascular permeability. Moreover, rapid and massive infusion can lead to the degradation, shedding and destruction of the EG. We aimed to explore the effects of different SVV values (11-14, 7-10 or 3-6%) on the EG in 54 patients who were scheduled for elective colorectal tumour surgery and to identify the optimal peri-operative fluid therapy strategy. The concentrations of EG degradation products (heparin sulphate, hyaluronic acid and syndecan-1) and atrial natriuretic peptide were higher when the SVV was maintained between 3 and 6% after fluid therapy compared with pre-infusion (P < 0.05). Comparison of postoperative complications and hospitalization time among the three SVV levels was not statistically significant (P > 0.05). The postoperative hospitalization time in patients with SVV of 7-10% was shorter than that in patients with SVV of 3-6%. Infusion of a large volume of fluid, with increasing EG degeneration and atrial natriuretic peptide concentrations, might be related to postoperative outcomes.

An integrated time adaptive geographic atrophy prediction model for SD-OCT images.

The automated prediction of geographic atrophy (GA) lesion growth can help ophthalmologists understand how the GA progresses, and assess the efficiency of current treatment and the prognosis of the disease. We developed an integrated time adaptive prediction model for identifying the location of future GA growth. The proposed model was comprised of bi-directional long short-term memory (BiLSTM) network-based prediction module and convolutional neural network (CNN)-based refinement module. Considering the discontinuity of time intervals among sequential follow-up visits, we integrated time factors into BiLSTM-based prediction module to control the time attribute expediently. Then, the results from prediction module were refined by a CNN-based strategy to obtain the final locations of future GA growth. The 10 scenarios were designed to evaluate the prediction accuracy of our proposed model. The 1-6th scenarios demonstrated the importance of the prior information similarity, the 7-8th scenarios verified the effect of time factors and refinement methods respectively and the 9th scenario compared the prediction results between those using a single follow-up visit for training and using 2 sequential follow-up visits for training. The 10th scenario showed the model generalization performance across regions. The average dice indexes (DI) of the predicted GA regions in the 1-6th scenarios are 0.86, 0.89, 0.89, 0.92 and 0.88, 0.90, respectively. By integrating time factors to the BiLSTM models, the prediction accuracy was improved by almost 10%. The CNN-based refinement strategy can remove the wrong GA regions effectively to preserve the actual GA regions and improve the prediction accuracy further. The prediction results based on 2 sequential follow-up visits showed higher correlations than that based on single follow-up visit. The proposed model presented a good generalization performance while training patients and testing patients were from different regions. Experimental results demonstrated the importance of prior information to the prediction accuracy. We demonstrate the feasibility of creating a model for disease prediction.

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.

As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions affected by GA is a fundamental step for clinical diagnosis. In this paper, we present a novel weakly supervised model for GA segmentation in Spectral-Domain Optical Coherence Tomography (SD-OCT) images. A novel Multi-Scale Class Activation Map (MS-CAM) is proposed to highlight the discriminatory significance regions in localization and detail descriptions. To extract available multi-scale features, we design a Scaling and UpSampling (SUS) module to balance the information content between features of different scales. To capture more discriminative features, an Attentional Fully Connected (AFC) module is proposed by introducing the attention mechanism into the fully connected operations to enhance the significant informative features and suppress less useful ones. Based on the location cues, the final GA region prediction is obtained by the projection segmentation of MS-CAM. The experimental results on two independent datasets demonstrate that the proposed weakly supervised model outperforms the conventional GA segmentation methods and can produce similar or superior accuracy comparing with fully supervised approaches. The source code has been released and is available on GitHub: https://github.com/ jizexuan/Multi-Scale-Class-Activation-Map-Tensorflow.

Image Projection Network: 3D to 2D Image Segmentation in OCTA Images.

We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.

Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy.

The presence of hyperreflective foci (HFs) is related to retinal disease progression, and the quantity has proven to be a prognostic factor of visual and anatomical outcome in various retinal diseases. However, lack of efficient quantitative tools for evaluating the HFs has deprived ophthalmologist of assessing the volume of HFs. For this reason, we propose an automated quantification algorithm to segment and quantify HFs in spectral domain optical coherence tomography (SD-OCT). The proposed algorithm consists of two parallel processes namely: region of interest (ROI) generation and HFs estimation. To generate the ROI, we use morphological reconstruction to obtain the reconstructed image and histogram constructed for data distributions and clustering. In parallel, we estimate the HFs by extracting the extremal regions from the connected regions obtained from a component tree. Finally, both the ROI and the HFs estimation process are merged to obtain the segmented HFs. The proposed algorithm was tested on 40 3D SD-OCT volumes from 40 patients diagnosed with non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and diabetic macular edema (DME). The average dice similarity coefficient (DSC) and correlation coefficient (r) are 69.70%, 0.99 for NPDR, 70.31%, 0.99 for PDR, and 71.30%, 0.99 for DME, respectively. The proposed algorithm can provide ophthalmologist with good HFs quantitative information, such as volume, size, and location of the HFs.

Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging.

BACKGROUND AND OBJECTIVE: Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images. METHODS: An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network. RESULTS: Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively. CONCLUSION: We report an automatic GA segmentation method utilizing synthesized FAF images. SIGNIFICANCE: Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.

Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.

PURPOSE: The purpose of this study was to automatically and accurately segment hyper-reflective foci (HRF) in spectral domain optical coherence tomography (SD-OCT) images with diabetic retinopathy (DR) using deep convolutional neural networks. METHODS: An automatic HRF segmentation model for SD-OCT images based on deep networks was constructed. The model segmented small lesions through pixel-wise predictions based on small image patches. We used an approach for discriminative features extraction for small patches by introducing small kernels and strides in convolutional and pooling layers, which was applied on the state-of-the-art deep classification networks (GoogLeNet and ResNet). The features extracted by the adapted deep networks were fed into a softmax layer to produce the probabilities of HRF. We trained different models on a dataset with 16 HRF eyes by using different sizes of patches, and then, we fused these models to generate optimal results. RESULTS: Experimental results on 18 eyes demonstrated that our method is effective for the HRF segmentation. The dice similarity coefficient (DSC) for the foci area in B-scan, projection images, and foci amount in B-scan images reaches 67.81%, 74.09%, and 72.45%, respectively. CONCLUSIONS: The proposed segmentation model can accurately segment HRF in SD-OCT images with DR and outperforms traditional methods. Our model may provide reliable segmentations for small lesions in SD-OCT images and may be helpful in the clinical diagnosis of diseases.

Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images.

BACKGROUND AND OBJECTIVE: Quantitative assessment of subretinal fluid in spectral domain optical coherence tomography (SD-OCT) images is crucial for the diagnosis of central serous chorioretinopathy. For the subretinal fluid segmentation, the traditional methods need to segment retinal layers and then segment subretinal fluid. The layer segmentation has a high influence on subretinal fluid segmentation, so we aim to develop a deep learning model to segment subretinal fluid automatically without layer segmentation. METHODS: In this paper, we propose a novel image-to-image double-branched and area-constraint fully convolutional networks (DA-FCN) for segmenting subretinal fluid in SD-OCT images. Firstly, the dataset is extended by mirroring image, which helps to overcome the over-fitting problem in the training stage. Then, double-branched structures are designed to learn the shallow coarse and deep representations from the SD-OCT images. DA-FCN model is directly trained using the image and corresponding pixel-based ground truth. Finally, we introduce a novel supervision mechanism by jointing the area loss LA with the softmax loss LS to learn more representative features. RESULTS: The testing dataset with 52 SD-OCT volumes from 35 eyes of 35 patients is used for the evaluation of the proposed algorithm based on the cross-validation method. For the three criterions, including the true positive volume fraction, dice similarity coefficient, and positive predicative value, our method can obtain the results of (1) 94.3, 95.3, and 96.4 for dataset 1; (2) 97.3, 95.3, and 93.4 for dataset 2; (3) 93.0, 92.8, and 92.8 for dataset 3; (4) 89.7, 90.1, and 92.6 for dataset 4. CONCLUSION: In this work, we propose a novel fully convolutional network for the automatic segmentation of the subretinal fluid. By constructing the double branched structures and area constraint term, our method shows higher segmentation accuracy without layer segmentation compared with other methods.