2019/08/28 by Kristine Aavild Juhl, Juhl, Kristine Aavild, Rasmus R. Paulsen +11
Computer Science · Engineering · Medicine · #Cerebrovascular and Carotid Artery Diseases #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Industrial Vision Systems and Defect Detection #Medical Image Segmentation Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.10579
openalex publication_date 2019/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.