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.

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.

Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation.

Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset.

Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model.

Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors.

A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans.

(BACKGROUND AND OBJECTIVES): Retinal cysts are formed by accumulation of fluid in the retina caused by leakages from inflammation or vitreous fractures. Analysis of the retinal cystic spaces holds significance in detection and treatment of several ocular diseases like age-related macular degeneration, diabetic macular edema etc. Thus, segmentation of intra-retinal cysts and quantification of cystic spaces are vital for retinal pathology and severity detection. In the recent years, automated segmentation of intra-retinal cysts using optical coherence tomography B-scans has gained significant importance in the field of retinal image analysis. The objective of this paper is to compare different intra-retinal cyst segmentation algorithms for comparative analysis and benchmarking purposes. (METHODS): In this work, we employ a modular approach for standardizing the different segmentation algorithms. Further, we analyze the variations in automated cyst segmentation performances and method scalability across image acquisition systems by using the publicly available cyst segmentation challenge dataset (OPTIMA cyst segmentation challenge). (RESULTS): Several key automated methods are comparatively analyzed using quantitative and qualitative experiments. Our analysis demonstrates the significance of variations in signal-to-noise ratio (SNR), retinal layer morphology and post-processing steps on the automated cyst segmentation processes. (CONCLUSION): This benchmarking study provides insights towards the scalability of automated processes across vendor-specific imaging modalities to provide guidance for retinal pathology diagnostics and treatment processes.

Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform.

Optical Coherence Tomography (OCT) has emerged as a major diagnostic modality for retinal imaging. Although OCT generates gross volumetric data, manual analysis of the images for locating or quantifying retinal cysts is a time consuming process. Recently semi- and fully-automatic methods for locating and segmenting retinal cysts have been proposed in the literature. Our paper proposes a fully automatic method for intra-retinal cyst segmentation using marker controlled watershed transform on B-scan images obtained on OCT scanning. Markers are obtained using k-means clustering and used as sources for topographical based watershed transform for final segmentation. Proposed method was evaluated both quantitatively and qualitatively on Optima Cyst Challenge dataset against ground truth obtained from two graders. Experimental results show that the proposed method outperformed other recently proposed methods. Our algorithm achieved a recall rate of 82% while preserving precision rate of 77%, and gave a higher correlation rate of 96% with ground truth obtained from two graders.

Is there a correlation between structural alterations and retinal sensitivity in morphological patterns of diabetic macular edema?

Spectral domain optical coherence tomography (SDOCT) enables enhanced visualization of retinal layers and delineation of structural alterations in diabetic macular edema (DME). Microperimetry (MP) is a new technique that allows fundus-related testing of local retinal sensitivity. Combination of these two techniques would enable a structure-function correlation with insights into pathomechanism of vision loss in DME. To correlate retinal structural derangement with retinal sensitivity alterations in cases with diabetic macular edema, using SDOCT and MP. Prospective study of 34 eyes of 30 patients with DME. All patients underwent comprehensive ophthalmic examination, fluorescein angiography, microperimetry and SDOCT. Four distinct morphological patterns of DME were identified- diffuse retinal thickening (DRT), cystoid macular edema (CME), schitic retinal thickening (SRT) and neurosensory detachment (NSD) of fovea. Some retinal loci presented with a mixture of above patterns There was significant difference in retinal thickness between groups (P<0.001). Focal retinal sensitivity measurement revealed relatively preserved retinal sensitivity in areas with DRT (13.8 dB), moderately reduced sensitivity (7.9 dB) in areas with CME, and gross retinal sensitivity loss in areas with SRT (1.2 dB) and NSD (4.7 dB) (P<0.001). Analysis of regional scotoma depth demonstrated similar pattern. Retinal sensitivity showed better correlation to OCT pattern (r=-0.68, P<0.001) than retinal thickness (r=-0.44, P<0.001). Structure-function correlation allows better understanding of the pathophysiology of visual loss in different morphological types of DME. Classification of macular edema into these categories has implications on the prognosis and predictive value of treatment.