Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

By Stephanie J. Chiu, Xiao T. Li, P. Nicholas, C. Toth, J. Izatt, Sina Farsiu. Optics Express, 2010

743 citations recorded by the source corpus.

Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

Subfields: Biomedical Engineering, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/86ee21d690eec2a73806c2086949e944f8b46f7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Leyuan Fang
2. Shutao Li
3. Yu Liu
4. Zengfu Wang

In Radiology, Nuclear Medicine and Imaging

1. Shutao Li
2. Leyuan Fang

Citations in the corpus

• Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017, methodology): “Finally, we use a modified variation of our popular graph theory and dynamic programming (GTDP) method [23], in which instead of using gradient based edge weights, we utilize these CNN based probability maps to create the final boundaries.”