Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI
Meissen F, Kaissis G, Rueckert D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12962 LNCS
Pages Range: 63-74
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783031089985
DOI: 10.1007/978-3-031-08999-2_5
Abstract
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions. (Code available under: https://github.com/FeliMe/brain_sas_baseline )
Involved external institutions
Germany (DE)How to cite
APA:
Meissen, F., Kaissis, G., & Rueckert, D. (2022). Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI. In Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 63-74). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Meissen, Felix, Georgios Kaissis, and Daniel Rueckert. "Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI." Proceedings of the 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Alessandro Crimi, Spyridon Bakas, Springer Science and Business Media Deutschland GmbH, 2022. 63-74.
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