Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy

Chronic Central Serous Chorioretinopathy in Elderly Subjects: Structure and Blood Flow Characteristics of Retina and Choroid

INTRODUCTION

With advancements in imaging technology, researchers have been able to identify more distinctive imaging features of central serous chorioretinopathy (CSC). However, existing research primarily concentrates on young patients aged 50 years and below, leaving a dearth of studies on elderly CSC patients. Previous studies indicate that elderly CSC patients may exhibit unique imaging characteristics and have a clinical prognosis that significantly differs from younger patients. This study aimed to evaluate the characteristics of retina, choroid structure, and blood flow in elderly patients with chronic CSC (cCSC) examined multimode imaging and try to find new pathogenesis information of it.

METHODS

Using a cut-off age of 50 years, patients with chronic central serous chorioretinopathy were divided into two groups: older and younger. The control group consisted of 40 healthy individuals, with their right eyes assigned. Various clinical features were recorded, including the incidence of ellipsoid zone rupture (EZ-), fibrin in the subretinal fluid (SRF), pachydrusen, subretinal drusenoid deposits (SDD), pigment epithelial detachment (PED), double-layer sign (DLS), and choroidal lipid globule cavern. Measurements were taken for the thickness of the outer nuclear layer (ONL), the length of the extended outer photoreceptor segment (POS), the height and width of SRF, the vascular density of each layer of the retinal capillary plexus, the central macular thickness (CMT), and the subfoveal choroidal thickness (SFCT).

RESULTS

The proportion of females in the elderly group (43.75%) was significantly higher than that in the youth group (22.41%) (p = 0.034). The degree of hyperopia in the elderly group (1.03 ± 0.73) was higher than that in the youth group (0.26 ± 1.06), with a significant difference in BCVA (p = 0.05). The thickness of SFCT, CMT, ONL in the elderly group, and the length of photoreceptor outer segment in the elderly group were thinner than those in the youth group (p < 0.05). Choroidal capillary perfusion area (CCPA), macular area, and paramacular area were lower in the elderly group than those in the youth group in the full scan range (p < 0.05). The blood flow densities of deep capillary plexus (DCP), intermediate capillary plexus (ICP), and superficial capillary plexus (SCP) in the whole scan range, macular area, and paramacular area were lower in the elderly group than in the youth group, but the differences were not statistically significant.

CONCLUSIONS

In conclusion, our data suggest that elderly patients with cCSC may experience different disease outcomes. Elderly cCSC patients exhibit less gender bias, poorer vision, more severe structural damage and ischemia in the choroid and retina, and have a higher risk of developing choroidal neovascularization.

Semi-supervised OCT lesion segmentation via transformation-consistent with uncertainty and self-deep supervision

Optical coherence tomography (OCT) is a non-invasive, high-resolution ocular imaging technique with important implications for the diagnosis and management of retinal diseases. Automatic segmentation of lesions in OCT images is critical for assessing disease progression and treatment outcomes. However, existing methods for lesion segmentation require numerous pixel-wise annotations, which are difficult and time-consuming to obtain. To address this challenge, we propose a novel framework for semi-supervised OCT lesion segmentation, termed transformation-consistent with uncertainty and self-deep supervision (TCUS). To address the issue of lesion area blurring in OCT images and unreliable predictions from the teacher network for unlabeled images, an uncertainty-guided transformation-consistent strategy is proposed. Transformation-consistent is used to enhance the unsupervised regularization effect. The student network gradually learns from meaningful and reliable targets by utilizing the uncertainty information from the teacher network, to alleviate the performance degradation caused by potential errors in the teacher network's prediction results. Additionally, self-deep supervision is used to acquire multi-scale information from labeled and unlabeled OCT images, enabling accurate segmentation of lesions of various sizes and shapes. Self-deep supervision significantly improves the accuracy of lesion segmentation in terms of the Dice coefficient. Experimental results on two OCT datasets demonstrate that the proposed TCUS outperforms state-of-the-art semi-supervised segmentation methods.

Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks

Objective. Retinal fluid mainly includes intra-retinal fluid (IRF), sub-retinal fluid (SRF) and pigment epithelial detachment (PED), whose accurate segmentation in optical coherence tomography (OCT) image is of great importance to the diagnosis and treatment of the relative fundus diseases. Approach. In this paper, a novel two-stage multi-class retinal fluid joint segmentation framework based on cascaded convolutional neural networks is proposed. In the pre-segmentation stage, a U-shape encoder–decoder network is adopted to acquire the retinal mask and generate a retinal relative distance map, which can provide the spatial prior information for the next fluid segmentation. In the fluid segmentation stage, an improved context attention and fusion network based on context shrinkage encode module and multi-scale and multi-category semantic supervision module (named as ICAF-Net) is proposed to jointly segment IRF, SRF and PED. Main results. the proposed segmentation framework was evaluated on the dataset of RETOUCH challenge. The average Dice similarity coefficient, intersection over union and accuracy (Acc) reach 76.39%, 64.03% and 99.32% respectively. Significance. The proposed framework can achieve good performance in the joint segmentation of multi-class fluid in retinal OCT images and outperforms some state-of-the-art segmentation networks.

Multi-Scale Pathological Fluid Segmentation in OCT With a Novel Curvature Loss in Convolutional Neural Network

The segmentation of pathological fluid lesions in optical coherence tomography (OCT), including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great importance for the diagnosis and treatment of various eye diseases such as neovascular age-related macular degeneration and diabetic macular edema. Although significant progress has been achieved with the rapid development of fully convolutional neural networks (FCN) in recent years, some important issues remain unsolved. First, pathological fluid lesions in OCT show large variations in location, size, and shape, imposing challenges on the design of FCN architecture. Second, fluid lesions should be continuous regions without holes inside. But the current architectures lack the capability to preserve the shape prior information. In this study, we introduce an FCN architecture for the simultaneous segmentation of three types of pathological fluid lesions in OCT. First, attention gate and spatial pyramid pooling modules are employed to improve the ability of the network to extract multi-scale objects. Then, we introduce a novel curvature regularization term in the loss function to incorporate shape prior information. The proposed method was extensively evaluated on public and clinical datasets with significantly improved performance compared with the state-of-the-art methods.

Capsule Network-based architectures for the segmentation of sub-retinal serous fluid in optical coherence tomography images of central serous chorioretinopathy

Central serous chorioretinopathy (CSCR) is a chorioretinal disorder of the eye characterized by serous detachment of the neurosensory retina at the posterior pole of the eye. CSCR results from the accumulation of subretinal fluid (SRF) due to idiopathic defects at the level of the retinal pigment epithelial (RPE) that allows serous fluid from the choriocapillaris to diffuse into the subretinal space between RPE and neurosensory retinal layers. This condition is presently investigated by clinicians using invasive angiography or non-invasive optical coherence tomography (OCT) imaging. OCT images provide a representation of the fluid underlying the retina, and in the absence of automated segmentation tools, currently only a qualitative assessment of the same is used to follow the progression of the disease. Automated segmentation of the SRF can prove to be extremely useful for the assessment of progression and for the timely management of CSCR. In this paper, we adopt an existing architecture called SegCaps, which is based on the recently introduced Capsule Networks concept, for the segmentation of SRF from CSCR OCT images. Furthermore, we propose an enhancement to SegCaps, which we have termed as DRIP-Caps, that utilizes the concepts of Dilation, Residual Connections, Inception Blocks, and Capsule Pooling to address the defined problem. The proposed model outperforms the benchmark UNet architecture while reducing the number of trainable parameters by 54.21%. Moreover, it reduces the computation complexity of SegCaps by reducing the number of trainable parameters by 37.85%, with competitive performance. The experiments demonstrate the generalizability of the proposed model, as evidenced by its remarkable performance even with a limited number of training samples. Graphical abstract is mandatory please provide.

Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning

Automated lesion segmentation is one of the important tasks for the quantitative assessment of retinal diseases in SD-OCT images. Recently, deep convolutional neural networks (CNN) have shown promising advancements in the field of automated image segmentation, whereas they always benefit from large-scale datasets with high-quality pixel-wise annotations. Unfortunately, obtaining accurate annotations is expensive in both human effort and finance. In this paper, we propose a weakly supervised two-stage learning architecture to detect and further segment central serous chorioretinopathy (CSC) retinal detachment with only image-level annotations. Specifically, in the first stage, a Located-CNN is designed to detect the location of lesion regions in the whole SD-OCT retinal images, and highlight the distinguishing regions. To generate available a pseudo pixel-level label, the conventional level set method is employed to refine the distinguishing regions. In the second stage, we customize the active-contour loss function in deep networks to achieve the effective segmentation of the lesion area. A challenging dataset is used to evaluate our proposed method, and the results demonstrate that the proposed method consistently outperforms some current models trained with a different level of supervision, and is even as competitive as those relying on stronger supervision. To our best knowledge, we are the first to achieve CSC segmentation in SD-OCT images using weakly supervised learning, which can greatly reduce the labeling efforts.

Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images

PURPOSE

Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here, we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer's lactate solution as samples.

DESIGN

Ex vivo animal study.

METHODS

Microscope-integrated optical coherence tomography was used to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center. Two different injection phases were imaged and analyzed in 15 eyes (30 volumes), selected from a total of 37 eyes. The inclusion/exclusion criteria were set independently from the algorithm-development and testing team. A novel lightweight, deep learning-based algorithm was designed to segment subretinal bleb boundaries. A cross-validation method was used to avoid selection bias. An ensemble-classifier strategy was applied to generate final results for the test dataset.

RESULTS

The algorithm performs notably better than 4 other state-of-the-art deep learning-based segmentation methods, achieving an F1 score of 93.86 ± 1.17% and 96.90 ± 0.59% on the independent test data for entry and full blebs, respectively.

CONCLUSION

The proposed algorithm accurately segmented the volumetric boundaries of Ringer's lactate solution delivered into the subretinal space of porcine eyes with robust performance and real-time speed. This is the first step for future applications in computer-guided delivery of therapeutics into the subretinal space in human subjects.

Correcting intra-volume distortion for AO-OCT using 3D correlation based registration

Adaptive optics (AO) based ophthalmic imagers, such as scanning laser ophthalmoscopes (SLO) and optical coherence tomography (OCT), are used to evaluate the structure and function of the retina with high contrast and resolution. Fixational eye movements during a raster-scanned image acquisition lead to intra-frame and intra-volume distortion, resulting in an inaccurate reproduction of the underlying retinal structure. For three-dimensional (3D) AO-OCT, segmentation-based and 3D correlation based registration methods have been applied to correct eye motion and achieve a high signal-to-noise ratio registered volume. This involves first selecting a reference volume, either manually or automatically, and registering the image/volume stream against the reference using correlation methods. However, even within the chosen reference volume, involuntary eye motion persists and affects the accuracy with which the 3D retinal structure is finally rendered. In this article, we introduced reference volume distortion correction for AO-OCT using 3D correlation based registration and demonstrate a significant improvement in registration performance via a few metrics. Conceptually, the general paradigm follows that developed previously for intra-frame distortion correction for 2D raster-scanned images, as in an AOSLO, but extended here across all three spatial dimensions via 3D correlation analyses. We performed a frequency analysis of eye motion traces before and after intra-volume correction and revealed how periodic artifacts in eye motion estimates are effectively reduced upon correction. Further, we quantified how the intra-volume distortions and periodic artifacts in the eye motion traces, in general, decrease with increasing AO-OCT acquisition speed. Overall, 3D correlation based registration with intra-volume correction significantly improved the visualization of retinal structure and estimation of fixational eye movements.

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans

Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as noise and motion artifacts present in OCT images, variation in size, shape and location of fluid pockets within the retina. The ability of a fully convolutional neural network to automatically learn significant low level features to differentiate subtle spatial variations makes it suitable for retinal fluid segmentation task. Hence, a fully convolutional neural network has been proposed in this work for the automatic segmentation of sub-retinal fluid in OCT scans of central serous chorioretinopathy (CSC) pathology. The proposed method has been evaluated on a dataset of 15 OCT volumes and an average Dice rate, Precision and Recall of 0.91, 0.93 and 0.89 respectively has been achieved over the test set.

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 L_A with the softmax loss L_S 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.

Artificial intelligence and machine learning for optical coherence tomography-based diagnosis in central serous chorioretinopathy

The aim of the present study was to examine the potential of machine learning for identification of isolated neurosensory retina detachment and retinal pigment epithelium (RPE) alterations as diagnostic criteria of central serous chorioretinopathy (CSC). Material and methods. Patients with acute CSC in whom a standard ophthalmic examination and optical coherence tomography (OCT) using RTVue-XR Avanti (Angio Retina HD scan protocol, 6 6 mm) was performed were included in the study. 10-m en face slab above the RPE layer was used to create ground truth masks. Learning aims were defined as identification of 3 classes of structural abnormalities on OCT cross-sectional scans: class 1 subretinal fluid, class 2 RPE abnormalities, and class 3 leakage points. Data for each of the 3 classes included: 4800/1400 training/test images for class 1, 2000/802 training/test images for class 2, and 1504/408 training/test images for class 3. Unet-similar architecture was used for segmentation of abnormalities on OCT cross-sectional scans. Results. Analysis of test sets revealed sensitivity, specificity, precision, and F1-score for detection of subretinal fluid of 0.61, 0.99, 0.99, and 0.76, respectively. For detection of RPE abnormalities sensitivity, specificity, precision, and F1-score were 0.14, 0.95, 0.94 and 0.24, respectively. For detection of leakage point sensitivity, specificity, precision, and F1-score were 0.06, 1.0, 1.0, and 0.12, respectively. Conclusions. Thus, machine learning demonstrated high potential in the OCT-based identification of structural abnormalities associated with acute CSC (neurosensory retina detachment and RPE alterations). Topical identification of the leakage point appears to be possible using large learning sets.

Detection of retinal pigment epithelium detachment from OCT images using multiscale Gaussian filtering

Macular diseases, including neovascular age-related macular degeneration (nvAMD), are leading causes of irreversible blindness and visual impairment. One prominent feature of nvAMD is the detachment of the retinal pigment epithelium. The aim of this study is to implement an automated method for the segmentation of the pigment epithelial detachment (PED) using optical coherence tomography (OCT). OCT datasets from 8 patients with nvAMD were acquired during multiple sessions. At each session, 17 images with a resolution of 1020 × 640 pixels were obtained. The images were segmented using Gaussian filtering and template matching for the detection of the upper and lower border of the PED, respectively. The results of the method were compared with the ones obtained from the manual segmentation of the images by an expert. Four well-known metrics were used to evaluate the performance of the method with respect to the manual segmentation, resulting in high scores of consistency. Furthermore, the proposed method was also compared with four other well-known methods providing similar or superior performance.

Automatic macular edema identification and characterization using OCT images

BACKGROUND AND OBJECTIVE

The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments.

METHODS

This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence.

RESULTS

The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset.

CONCLUSIONS

The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.