Abstract
Optic disc (OD) is the main anatomical structures in retinal images. It is very important to conduct reliable OD segmentation in the automatic diagnosis of many fundus diseases. For OD segmentation, the previous studies with stacked convolutional layers and pooling operations often neglect the detailed spatial information. However, this information is vital to distinguish the diversity of the profile of OD and the spatial distribution of vessels. In this paper, we propose a novel OD segmentation network by designing two modules, namely, light U-Net module and atrous convolution spatial pyramid pooling module. We first extract hierarchical features by using ResNet-101 as a base network. Light U-Net module is utilized to learn the intrinsic spatial information effectively and enhance the ability of feature representation in low-level feature maps. Atrous convolution and spatial pyramid pooling module is used to incorporate global spatial information in high-level semantic features. Finally, we integrate the spatial information by feature fusion to get the segmentation results. We estimate the proposed method on two public retinal fundus image datasets (REFUGE and Drishti-GS). For the REFUGE dataset, our model achieves about 2% improvement in the mIoU and Dice over the next best method. For Drishti-GS, our method also outperforms the other state-of-the-art methods with 99.74% Dice and 93.26% mIoU.
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Acknowledgements
This work was supported partly by National Natural Science Foundation of China (Nos. 61871274, 61801305 and 81571758), National Natural Science Foundation of Guangdong Province (No. 2020A1515010649 and No. 2019A1515 111205), Guangdong Province Key Laboratory of Popular High Performance Computers (No. 2017B030314073), Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), Shenzhen Peacock Plan (Nos. KQTD2016053112051497 and KQTD2015033016104926), Shenzhen Key Basic Research Project (Nos. JCYJ201908 08165209410, 20190808145011259, JCYJ20180507184647636, GJHZ20190822095 414576 and JCYJ20170302153337765, JCYJ20170302150411789, JCYJ2017030214 2515949, GCZX2017040715180580, GJHZ20180418190529516, and JSGG2018050 7183215520), NTUT-SZU Joint Research Program (No. 2020003), Special Project in Key Areas of Ordinary Universities of Guangdong Province (No. 2019KZDZX1015).
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Liu, W., Lei, H., Xie, H., Zhao, B., Yue, G., Lei, B. (2020). Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_11
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