3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
This work addresses the challenge of accurate brain MRI segmentation for medical applications by leveraging shape priors, representing an incremental improvement over existing methods that often fail to specifically learn shape features.
The paper tackles the problem of incorporating shape priors into medical image segmentation by introducing a novel approach that integrates shape information directly into the segmentation network, resulting in improved performance with lower Hausdorff distance and higher Dice coefficient on brain MRI segmentation compared to state-of-the-art methods.
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.