Densely encoded attention networks for accurate retinal layers segmentation in optical coherence tomography

Ying Ma; Jiabao Jin; Guanping Xu; Ao Wang; Yinran Chen; Weijie Ouyang; Xiang Lin; Zuguo Liu; Xiongbiao Luo

doi:10.1117/12.2653924

7 April 2023 Densely encoded attention networks for accurate retinal layers segmentation in optical coherence tomography

Ying Ma, Jiabao Jin, Guanping Xu, Ao Wang, Yinran Chen, Weijie Ouyang, Xiang Lin, Zuguo Liu, Xiongbiao Luo

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124653C (2023) https://doi.org/10.1117/12.2653924
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Optical coherence tomography (OCT) is a non-invasive imaging modality that suitable for accessing retinal diseases. Since the thickness and shape of the retinal layer are diagnostic indicators for many ophthalmic diseases, segmentation of the retinal layer in OCT images is a critical step. Automated segmentation of oct images has made many efforts but there are still some challenges, such as lack of context information, ambiguous boundaries and inconsistent prediction of retinal lesion regions. In this work, we propose a new framework of Densely Encoded Attention Networks (DEAN) that combines dense encoders with position attention in an U-architecture for retinal layers segmentation. Since the spatial position of each layer in OCT image is relatively fixed, we use convolution in dense connections to obtain diverse feature maps in the encoder and employ position attention to improve the spatial information of learning targets. Moreover, up-sampling and skip connections in the decoder are to restore resolution by the position index saved during down-sampling, while supplementing the corresponding pixels is to guide the network capturing the global context information. This method is evaluated on two public datasets, and the results demonstrate that our method is an effective strategy on improving the performance of segmenting the retinal layers.

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Ying Ma, Jiabao Jin, Guanping Xu, Ao Wang, Yinran Chen, Weijie Ouyang, Xiang Lin, Zuguo Liu, and Xiongbiao Luo "Densely encoded attention networks for accurate retinal layers segmentation in optical coherence tomography", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124653C (7 April 2023); https://doi.org/10.1117/12.2653924

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