Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network. (April 2021) - Wang, Lei; Gu, Juan; Chen, Yize; Liang, Yuanbo; Zhang, Weijie; Pu, Jiantao; Chen, Hao
Highlights: A novel deep learning network was proposed based on the classical U-Net model to accurately segment the optic disc from colour fundus images. A sub-network and a decoding convolutional block were introduced to provide additional key features and highlight the morphological changes of the target objects in convolutional feature maps. Experiment results on both the global field-of-view fundus images and their local disc versions from the MESSIDOR, ORIGA, and REFUGE datasets demonstrated that the developed network achieved promising performance and outperformed some existing segmentation networks. Abstract: Accurate segmentation of the optic disc (OD) regions from colour fundus images is a critical procedure for computer-aided diagnosis of glaucoma. We present a novel deep learning network to automatically identify the OD regions. On the basis of the classical U-Net framework, we define a unique sub-network and a decoding convolutional block. The sub-network is used to preserve important textures and facilitate their detections, while the decoding block is used to improve the contrast of the regions-of-interest with their background. We integrate these two components into the classical U-Net framework to improve the accuracy and reliability of segmenting the OD regions depicted on colour fundus images. We train and evaluate the developed network using three publicly available datasets ( i.e., MESSIDOR, ORIGA, and REFUGE). The results on an independent testing set ( n = 1, 970 images) show a segmentation performance with an average Dice similarity coefficient (DSC), intersection over union (IOU), and Matthew's correlation coefficient (MCC) of 0.9377, 0.8854, and 0.9383 when trained on the global field-of-view images, respectively, and 0.9735, 0.9494, and 0.9594 when trained on the local disc region images. When compared with the other three classical networks ( i.e., the U-Net, M-Net, and Deeplabv3) on the same testing datasets, the developed network demonstrates a relatively higher performance.