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首页> 外文会议>Conference on Medical Imaging: Image Processing >Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module
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Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module

机译：基于改进U形网络的跨融合全球特征模块分割了视网膜脱离和视网膜视网膜isis

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摘要

Retinal detachment (RD) refers to the separation of the retinal neuroepithelium layer (RNE) and retinal pigment epithelium (RPE), and retinoschisis (RS) is characterized by the RNE splitting into multiple layers. Retinal detachment and retinoschisis are the main complications leading to vision loss in high myopia. Optical coherence tomography (OCT) is the main imaging method for observing retinal detachment and retinoschisis. This paper proposes a U-shaped convolutional neural network with a cross-fusion global feature module (CFCNN) to achieve automatic segmentation of retinal detachment and retinoschisis. Main contributions include: (1) A new cross-fusion global feature module (CFGF) is proposed. (2) The residual block is integrated into the encoder of the U-Net network to enhance the extraction of semantic information. The method was tested on a dataset consisting of 540 OCT B-scans. With the proposed CFCNN method, the mean Dice similarity coefficient of retinal detachment and retinoschisis segmentation reached 94.33% and 90.29% and were better than some existing advanced segmentation networks.
机译：视网膜脱离（RD）是指视网膜神经沉积物层（RNE）和视网膜颜料上皮（RPE）的分离，并且视黄芩（RS）的特征在于将RNE分成多层。视网膜脱离和视网膜是一种主要的并发症，导致高近视的视力丧失。光学相干断层扫描（OCT）是用于观察视网膜脱离和视网膜的主要成像方法。本文提出了一种具有交叉融合全球特征模块（CFCNN）的U形卷积神经网络，以实现视网膜脱离和视网膜的自动分割。主要贡献包括：（1）提出了一种新的交叉融合全局功能模块（CFGF）。 （2）剩余块被集成到U-Net网络的编码器中，以增强语义信息的提取。该方法在由540华侨城B扫描组成的数据集上进行测试。利用所提出的CFCNN方法，视网膜脱离和视网膜裂缝分割的平均骰子相似度系数达到94.33％和90.29％，而且比现有的先进分割网络更好。

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