Cascaded multi-scale feature interaction for choroidal atrophy segmentation

Jiahuan Song; Xinjian Chen; Weifang Zhu

doi:10.1117/12.2580652

15 February 2021 Cascaded multi-scale feature interaction for choroidal atrophy segmentation

Jiahuan Song, Xinjian Chen, Weifang Zhu

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960J (2021) https://doi.org/10.1117/12.2580652
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In this paper, we propose a new module called cascaded multi-scale feature interaction (CMSI) for choroidal atrophy segmentation in fundus images. The proposed CMSI module makes full use of multi-scale features, including using cascaded pooling and convolution to complete feature interactions at different scales and using strip pooling to capture long-distance features. Based on the U-shape network, we use the ResNet as the backbone to extract hierarchical feature representations. The proposed CMSI module is added at the top of the encoder path. In summary, our main contributions are summarized in two aspects as follows: (1) The CMSI module is proposed for multi-scale feature ensembling by cascading multi-scale pooling and strip pooling. (2) The Dice coefficients of our proposed network on choroidal atrophy segmentation increased by 4.15% compared to U-Net.

Conference Presentation

Citation Download Citation

Jiahuan Song, Xinjian Chen, and Weifang Zhu "Cascaded multi-scale feature interaction for choroidal atrophy segmentation", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960J (15 February 2021); https://doi.org/10.1117/12.2580652

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