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Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting

Nguyen, Bao; Feldman, Adam; Bethapudi, Sarath; Jennings, Andrew; Willcocks, Chris G

Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting Thumbnail


Authors

Bao Nguyen

Adam Feldman

Sarath Bethapudi

Andrew Jennings



Abstract

Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively.

Citation

Nguyen, B., Feldman, A., Bethapudi, S., Jennings, A., & Willcocks, C. G. (2021). Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting. . https://doi.org/10.1109/isbi48211.2021.9434115

Conference Name 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Conference Location Nice, France
Start Date Apr 13, 2021
End Date Apr 16, 2021
Online Publication Date May 25, 2021
Publication Date 2021
Deposit Date Nov 27, 2020
Publicly Available Date Oct 28, 2021
Publisher Institute of Electrical and Electronics Engineers
Pages 1127-1131
DOI https://doi.org/10.1109/isbi48211.2021.9434115

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