Robust segmentation via topology violation detection and feature synthesiss
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Despite recent progress of deep learning-based medical image segmentation techniques, fully automatic results often fail to meet clinically acceptable accuracy, especially when topological constraints should be observed, e.g., closed surfaces. Although modern image segmentation methods show promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Union, these metrics do not reflect the correctness of a segmentation in terms of a required topological genus. Existing approaches estimate and constrain the topological structure via persistent homology (PH). However, these methods are not computationally efficient as calculating PH is not differentiable. To overcome this problem, we propose a novel approach for topological constraints based on the multi-scale Euler Characteristic (EC). To mitigate computational complexity, we propose a fast formulation for the EC that can inform the learning process of arbitrary segmentation networks via topological violation maps. Topological performance is further facilitated through a corrective convolutional network block. Our experiments on two datasets show that our method can significantly improve topological correctness.
Editor(s)
Greenspan, H
Madabhushi, A
Mousavi, P
Salcudean, S
Duncan, J
Syeda-Mahmood, T
Taylor, R
Date Issued
2023-10-01
Date Acceptance
2023-10-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14223, pp.67-77
ISBN
978-3-031-43900-1
ISSN
0302-9743
Publisher
Springer
Start Page
67
End Page
77
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
14223
Copyright Statement
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Source
26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Subjects
Computer Science
Computer Science, Software Engineering
Computer Science, Theory & Methods
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
RECONSTRUCTION
Science & Technology
Technology
Publication Status
Published
Start Date
2023-10-08
Finish Date
2023-10-12
Coverage Spatial
Vancouver, Canada