Abstract
Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.
Highlights
Because the vision loss caused by glaucoma is irreversible [1], early screening for glaucoma disease is important
The automatic segmentation method of the fundus image optic disc is mainly divided into two categories, methods based on image processing and hand-made features, and methods based on deep learning
Inspired by the successful application of the channel attention mechanism in the field of medical image segmentation [19,20,21], we introduced an aggregation channel attention network to improve the performance of optic disc segmentation of fundus images
Summary
Because the vision loss caused by glaucoma is irreversible [1], early screening for glaucoma disease is important. In the large-scale screening of glaucoma diseases, an automated method that saves manpower is needed. The cup-to-disk ratio (CDR) [2] of the fundus image is an important indicator for clinical diagnosis of glaucoma. The greater the CDR, the greater the risk of glaucoma, and vice versa. Using computer technology to segment the fundus image becomes the key. The automatic segmentation method of the fundus image optic disc is mainly divided into two categories, methods based on image processing and hand-made features, and methods based on deep learning
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