Automated geographic atrophy segmentation with multi-loss for SD-OCT images based on patient independent

Jiwen Dong; Junting Chen; Xizhan Gao; Rongbin Xu; Sijie Niu

doi:10.1117/12.2589371

27 January 2021 Automated geographic atrophy segmentation with multi-loss for SD-OCT images based on patient independent

Jiwen Dong, Junting Chen, Xizhan Gao, Rongbin Xu, Sijie Niu

Author Affiliations +

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172012 (2021) https://doi.org/10.1117/12.2589371
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

The geographic atrophy (GA) caused by retinal layer atrophy is an important clinical manifestation of age- related macular disease (AMD). Automatic segmentation for GA in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. In this paper, we proposed a multi-loss convolutional neural network for GA automatic segmentation based on patient independent. Firstly, to overcome the shortness of samples in medical image processing, the proposed method augmented the samples with samples reversing. Then the model used multi-path block structure to replace single structure of classical CNN to enrich the diversity of features. And the multi-path block loss, cross entropy, and center loss were adapted to supervise and optimize the network effectively, thus it can force the network to learn more representative features. Finally, two data sets were used to evaluate the performance of the model, it demonstrated that the result has a high overlap ratio, correlation coefficient and low absolute area difference. The average overlap ratios on two data sets are 81.88% and 66.86% respectively.

Citation Download Citation

Jiwen Dong, Junting Chen, Xizhan Gao, Rongbin Xu, and Sijie Niu "Automated geographic atrophy segmentation with multi-loss for SD-OCT images based on patient independent", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172012 (27 January 2021); https://doi.org/10.1117/12.2589371

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