Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations

Liu，Hong1,2,3; Wei，Dong3; Lu，Donghuan3; Tang，Xiaoying4; Wang，Liansheng1; Zheng，Yefeng3

2024

10.1016/j.media.2023.103019

Medical Image Analysis   影响因子和分区

1361-8415

1361-8423

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D–3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers’ natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

2D–3D hybrid network 3D coherent layer segmentation B-scan alignment Optical coherence tomography Semi-supervised learning with sparse annotation

SCI ; EI

英语

其他

COMPUTER SCIENCE

2-s2.0-85176354471

Scopus

1.School of Informatics,Xiamen University,Xiamen,361005,China
2.National Institute for Data Science in Health and Medicine,Xiamen University,Xiamen,361005,China
3.Jarvis Research Center,Tencent YouTu Lab,Shenzhen,518075,China
4.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Liu，Hong,Wei，Dong,Lu，Donghuan,等. Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations[J]. Medical Image Analysis,2024,91.

Liu，Hong,Wei，Dong,Lu，Donghuan,Tang，Xiaoying,Wang，Liansheng,&Zheng，Yefeng.(2024).Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations.Medical Image Analysis,91.

Liu，Hong,et al."Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations".Medical Image Analysis 91(2024).