2019/09/14 by Zhou He, He Zhou, He, Zhou +5
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1909.06629
Accepted to 2018 MICCAI DLMIA, published at Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
openalex publication_date 2019/09/14 · arxiv created 2019/09/17 · arxiv updated 2019/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.