Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans

Amr Elsawy; Tiarnan D. Kenan; Qingyu Chen; Xiaoshuang Shi; Emily Y. Chew; Zhiyong Lu

doi:10.1117/12.2654487

3 April 2023 Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans

Amr Elsawy, Tiarnan D. Kenan, Qingyu Chen, Xiaoshuang Shi, Emily Y. Chew, Zhiyong Lu

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124643B (2023) https://doi.org/10.1117/12.2654487
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Geographic atrophy (GA) is the defining lesion of advanced atrophic age-related macular degeneration (AMD). GA can be detected and characterized most accurately using spectral-domain optical coherence tomography (SDOCT), which provides detailed 3D information about changes in multiple retinal layers. Existing methods are limited to 2D convolutional neural networks (CNNs). Therefore, they do not capture the 3D context between adjacent 2D slices of the OCT scan and also require a large inference time. We propose 3D CNNs with 3D attention mechanisms for the automated detection of GA on SDOCT scans using scan-level labels. The best network achieved an accuracy of 88%, and its visualizations suggest the interpretability of its predictions.

Citation Download Citation

Amr Elsawy, Tiarnan D. Kenan, Qingyu Chen, Xiaoshuang Shi, Emily Y. Chew, and Zhiyong Lu "Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643B (3 April 2023); https://doi.org/10.1117/12.2654487

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