Fetal cortex segmentation with topology and thickness loss constraints
Author(s)
Type
Conference Paper
Abstract
The segmentation of the fetal cerebral cortex from magnetic resonance imaging (MRI) is an important tool for neurobiological research about the developing human brain. Manual segmentation is difficult and time-consuming. Limited image resolution and partial volume effects introduce errors and labeling noise when attempting to automate the process through machine learning. The significant morphological changes observed during brain growth pose additional challenges for learning-based image segmentation methods, which may drastically increase the amount of necessary training data. In this paper, we propose a framework to learn from noisy labels by using additional regularization via shape priors for the accurate segmentation of the cortical gray matter (CGM) in 3D. Firstly, we introduce a novel structure consistency loss based on persistent homology analysis of the cortical topology. Secondly, a regularization loss term is proposed by integrating assumptions about the cortical thickness within each sample. Our experiments on the developing human connectome project (dHCP) dataset show that our method can predict accurate CGM segmentation learned from noisy labels.
Editor(s)
Baxter, JSH
Rekik, I
Eagleson, R
Zhou, L
Syeda-Mahmood, T
Wang, H
Hajij, M
Date Issued
2022-12-20
Date Acceptance
2022-09-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13755, pp.123-133
ISBN
978-3-031-23222-0
ISSN
0302-9743
Publisher
Springer
Start Page
123
End Page
133
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
13755
Copyright Statement
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-23223-7_11
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000913354000011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Source
1st Workshop on Ethical and Philosop Issues in Med Imaging (EPIMI) / 12th Int Workshop on Multimodal Learning and Fus Across Scales for Clin Decis Support (ML-CDS) / 2nd Int Workshop on Topol Data Anal for Biomed Imaging (TDA4BiomedicalImaging)
Subjects
Arts & Humanities
BRAIN MRI
Computer Science
Computer Science, Interdisciplinary Applications
History & Philosophy of Science
History & Philosophy Of Science
Life Sciences & Biomedicine
Medical Ethics
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Technology
VOLUME RECONSTRUCTION
Publication Status
Published
Start Date
2022-09-18
Finish Date
2022-09-22
Coverage Spatial
Singapore
Date Publish Online
2022-12-20