@inproceedings{c2d6a20d84074ac0b9cb79fbd3319fdd,
title = "Enforcing Connectivity of 3D Linear Structures Using Their 2D Projections",
abstract = "Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data",
keywords = "Delineation, Microscopy scans, Neurons, Topology",
author = "Doruk Oner and Hussein Osman and Mateusz Kozi{\'n}ski and Pascal Fua",
year = "2022",
doi = "10.1007/978-3-031-16443-9_57",
language = "English",
isbn = "9783031164422",
series = "Lecture Notes in Computer Science ",
publisher = "Springer",
pages = "591–601",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
note = "25th International Conference on Medical Image Computing and Computer Assisted Intervention : MICCAI 2022, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
}