Fetal Cortex Segmentation with Topology and Thickness Loss Constraints

Liu Li*, Qiang Ma, Zeju Li, Cheng Ouyang, Weitong Zhang, Anthony Price, Vanessa Kyriakopoulou, Lucilio C. Grande, Antonis Makropoulos, Joseph Hajnal, Daniel Rueckert, Bernhard Kainz, Amir Alansary

*Corresponding author for this work

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

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationEthical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging - 1st International Workshop, EPIMI 2022, 12th International Workshop, ML-CDS 2022, 2nd International Workshop, TDA4BiomedicalImaging, Held in Conjunction with MICCAI 2022, Proceedings
EditorsJohn S.H. Baxter, Islem Rekik, Roy Eagleson, Luping Zhou, Tanveer Syeda-Mahmood, Hongzhi Wang, Mustafa Hajij
PublisherSpringer Science and Business Media Deutschland GmbH
Pages123-133
Number of pages11
ISBN (Print)9783031232220
DOIs
Publication statusPublished - 2022
Event1st International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2022, the 12th International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2022, and the 2nd International Workshop on Topological Data Analysis for Biomedical Imaging, TDA4BiomedicalImaging 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202222 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13755 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2022, the 12th International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2022, and the 2nd International Workshop on Topological Data Analysis for Biomedical Imaging, TDA4BiomedicalImaging 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/202222/09/2022

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