We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

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

Nguyen, Bao and Feldman, Adam and Bethapudi, Sarath and Jennings, Andrew and Willcocks, Chris G. (2021) 'Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting.', 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) Nice, France, 13 - 16 April 2021.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:28 October 2021
Date of first online publication:25 May 2021
Date first made open access:28 October 2021

Save or Share this output

Look up in GoogleScholar