Segmentation of retinal detachment and retinoschisis in OCT images based on complementary multi-class segmentation networks

Abstract

Retinal detachment (RD) and retinoschisis (RS) are the main complications leading to vision loss in high myopia. Accurate segmentation of RD and RS, including its subcategories (outer, middle, and inner retinoschisis) in optical coherence tomography (OCT) images is of great clinical significance in the diagnosis and management of high myopia. For this multi-class segmentation task, we propose a novel framework named complementary multi-class segmentation networks (CMC-Net). Based on domain knowledge, a three-class segmentation path (TSP) and a five-class segmentation path (FSP) are designed, and their outputs are integrated through additional decision fusion layers to achieve improved segmentation in a complementary manner. In TSP, a cross-fusion global feature module (CFGF) is adopted to achieve global receptive field. In FSP, a novel three-dimensional contextual information perception module (TCIP) is proposed to capture long-range contexts, and a classification branch is designed to provide useful features for segmentation. A new category loss is also proposed in FSP to help better identify the lesion categories. Experiment results show that the proposed method achieves superior performance for joint segmentation of RD and the three subcategories of RS, with an average Dice coefficient of 84.83%.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images

“…To solve the problems with the conventional segmentation techniques, many deep learning (DL) models have been proposed (Kamnitsas et al 2017, Zhao et al 2020, Chen et al 2023, Shi et al 2023. Among these models, the convolutional neural network (CNN) (Ronneberger et al 2015, Lian et al 2018 and transformer (Vaswani et al 2017, Tao et al 2022 have become two kinds of popular models for segmentation tasks.…”