Recent advancements in medical image segmentation techniques have achieved compelling results. However, most of the widely used approaches do not take into account any prior knowledge about the shape of the biomedical structures being segmented. More recently, some works have presented approaches to incorporate shape information. However, many of them are indeed introducing more parameters to the segmentation network to learn the general features, which any segmentation network is able learn, instead of specifically shape features. In this paper, we present a novel approach that seamlessly integrates the shape information into the segmentation network. Experiments on human brain MRI segmentation demonstrate that our approach can achieve a lower Hausdorff distance and higher Dice coefficient than the state-of-the-art approaches.
@article{arxiv.1909.06629,
title = {3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation},
author = {Zhou He and Siqi Bao and Albert Chung},
journal= {arXiv preprint arXiv:1909.06629},
year = {2019}
}
Comments
Accepted to 2018 MICCAI DLMIA, published at Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support