Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth

Hassna Irzan*, Lucas Fidon, Tom Vercauteren, Sebastien Ourselin, Neil Marlow, Andrew Melbourne

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Neuroimaging studies of structural connectomes typically average the data from many subjects and analyse the average properties of the resulting network. We propose a new framework for individual brain-network structural abnormality detection. The framework uses a graph-based anomaly detection algorithm that allows to detect abnormal structural connectivity on a subject level. The proposed method is generic and can be adapted for a broad range of network abnormality detection problems. In this study, we apply our method to investigate the integrity of white matter tracts of 19-year-old extremely preterm born individuals. We show the feasibility to cast the network abnormality detection problem into a min-cut max-flow problem, and identify consistent abnormal white matter tracts in extremely preterm subjects, including a common network involving the bilateral thalamus and frontal gyri.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis - 2nd International Workshop, UNSURE 2020, and 3rd International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsCarole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot
PublisherSpringer Science and Business Media Deutschland GmbH
Pages164-173
Number of pages10
ISBN (Print)9783030603649
DOIs
Publication statusPublished - 2020
Event2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 8 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the 3rd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period8/10/20208/10/2020

Fingerprint

Dive into the research topics of 'Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth'. Together they form a unique fingerprint.

Cite this