Locate before Segment: Topology-guided Retinal Layer Segmentation in Optical Coherence Tomography Images

Ye Lu1; Yutian Shen1; Xiaohan Xing2; Max Q.-H. Meng3

10.1109/ICRA48891.2023.10160300

2023-05-29

IEEE International Conference on Robotics and Automation (ICRA)

1050-4729

2577-087X

979-8-3503-2366-5

2023 IEEE International Conference on Robotics and Automation (ICRA)

2023-May

4775-4781

29 May-2 June 2023

London, United Kingdom

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Optical Coherence Tomography (OCT) is a non-invasive imaging technique that is instrumental in retinal disease diagnosis and treatment. Segmentation of retinal layers in OCT is an essential step, but remains challenging for common pixel-wise segmentation methods usually fail to obtain the correct layer topology. To tackle this challenge, we propose a novel Locate-to-Segment (L2S) framework to provide a layer region location guidance for pixel-wise labeling learning so as to obtain better segmentation with the correct topology and smooth boundaries. Specifically, a Structured Boundary Regression Network (SBRNet) is devised to first predict the surface positions. For effective learning on normal-size images, we design two regression branches to regress the top surface and eight layer widths separately in SBRNet to locate each layer region with absolutely correct orderings. Then, we take the prediction of SBRNet as an additional input for a common pixel-wise segmentation network to provide the guidance of correct topology. In this L2S manner, our framework takes merits of regression-based methods and pixel-wise labeling-based methods to obtain accurate segmentation with the correct topology and smooth continuous boundaries. Experimental results on a public retinal OCT dataset demonstrate the effectiveness of our method, outperforming state-of-the-art segmentation methods with the highest average Dice score of 90.29% and the lowest average MAD score of 0.782.

Image segmentation Automation Network topology Optical coherence tomography Instruments Imaging Retina

其他

英语

EI ; CPCI-S

National Key R&D program of China[2019YFB1312400]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:001036713003104

20233514632962

Diagnosis ; Image segmentation ; Medical imaging ; Ophthalmology ; Pixels ; Topology

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

IEEE

1.Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
2.Department of Electronic and Electrical Engineering, Shenzhen Key Laboratory of Robotics Perception and Intelligence, Southern University of Science and Technology, Shenzhen, China
3.Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong

Ye Lu,Yutian Shen,Xiaohan Xing,et al. Locate before Segment: Topology-guided Retinal Layer Segmentation in Optical Coherence Tomography Images[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:4775-4781.