3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation release_6s2ecljv3jgrrcgz53ahgfie74

by Zhou He, Siqi Bao, Albert Chung

Released as a article .

2019  

Abstract

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.
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Type  article
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Date   2019-09-17
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Language   en ?
arXiv  1909.06629v2
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