Enforcing Connectivity of 3D Linear Structures Using Their 2D Projections

Doruk Oner*, Hussein Osman, Mateusz Koziński, Pascal Fua

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Place of PublicationCham
Number of pages11
ISBN (Print)9783031164422
Publication statusPublished - 2022
Event25th International Conference on Medical Image Computing and Computer Assisted Intervention: MICCAI 2022 - Singapur, Singapore
Duration: 18 Sept 202222 Sept 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2022


  • Delineation
  • Microscopy scans
  • Neurons
  • Topology

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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