Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation

Shuo Han; Jerry L. Prince; Aaron Carass

doi:10.1117/12.2549399

10 March 2020 Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation

Shuo Han, Jerry L. Prince, Aaron Carass

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131337 (2020) https://doi.org/10.1117/12.2549399
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Convolutional neural networks (CNNs) have been successfully applied to human brain segmentation. To in- corporate the left and right symmetry property of the brain into a network architecture, we propose a 3D left-right-reflection equivariant network to segment the anatomical structures of the brain. We extended previous group convolutions to account for left-right paired labels in the delineation. The proposed networks were compared with conventional networks trained with left-right reflection data augmentation in several tasks, showing improved performance. This is also the first work to extend reflection-equivariant CNNs to left-right paired labels in the human brain.

Citation Download Citation

Shuo Han, Jerry L. Prince, and Aaron Carass "Reflection-equivariant convolutional neural networks improve segmentation over reflection augmentation", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131337 (10 March 2020); https://doi.org/10.1117/12.2549399

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