Automated segmentation and extraction of posterior eye segment using OCT scans
This paper proposes an automated method for the segmentation and extraction of the posterior segment of the human eye, including the vitreous, retina, choroid, and sclera compartments, using multi-vendor optical coherence tomography (OCT) scans. The proposed method works in two phases. First extracts the retinal pigment epithelium (RPE) layer by applying the adaptive thresholding technique to identify the retina-choroid junction. Then, it exploits the structure tensor guided approach to extract the inner limiting membrane (ILM) and the choroidal stroma (CS) layers, locating the vitreous-retina and choroid-sclera junctions in the candidate OCT scan. Furthermore, these three junction boundaries are utilized to conduct posterior eye compartmentalization effectively for both healthy and disease eye OCT scans. The proposed framework is evaluated over 1000 OCT scans, where it obtained the mean intersection over union (IoU) and mean Dice similarity coefficient (DSC) scores of 0.874 and 0.930, respectively.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT
Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected component…
Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, ma…
Computed Tomography (CT)Liver SegmentationSegmentationA Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis. For anatomically high-variability abdominal organs such as the pancreas, previous …
ClusteringComputed Tomography (CT)Organ SegmentationPancreas Segmentation+2Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation
Automated vehicles rely on an accurate and robust perception of the environment. Similarly to automated cars, highly automated trains require an environmental perception. Although there is a lot of research based on eith…
3D Semantic SegmentationActive LearningImage SegmentationLIDAR Semantic Segmentation+2Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. Th…