# Automated segmentation and extraction of posterior eye segment using OCT scans

arXiv: https://arxiv.org/abs/2109.10000 · HTML: https://mlsanity.com/paper/2109.10000/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression](https://mlsanity.com/paper/2010.03872/) (2020)
- [Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans](https://mlsanity.com/paper/2004.06780/) (2020)
- [SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression](https://mlsanity.com/paper/2003.00825/) (2020)
- [Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition](https://mlsanity.com/paper/2107.07333/) (2021)
- [Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion](https://mlsanity.com/paper/2009.13158/) (2020)
- [A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images](https://mlsanity.com/paper/2011.00527/) (2020)
- [Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition](https://mlsanity.com/paper/2108.09603/) (2021)

## Built on by (most cited first, 2 of 2)

- [Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning](https://mlsanity.com/paper/2110.09319/) (2021)
- [Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats](https://mlsanity.com/paper/2111.02651/) (2021)

## Abstract (arXiv)

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.