Irzan, H;
Fidon, L;
Vercauteren, T;
Ourselin, S;
Marlow, N;
Melbourne, A;
(2020)
Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth.
In:
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging International. Workshop on Graphs in Biomedical Image Analysis.
(pp. pp. 164-173).
Springer: Cham, Switzerland.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth |
| Event: | Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020 |
| ISBN-13: | 978-3-030-60364-9 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-60365-6_16 |
| Publisher version: | https://doi.org/10.1007/978-3-030-60365-6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10125866 |
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