Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
Baur C, Wiestler B, Albarqouni S, Navab N (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11383 LNCS
Pages Range: 161-169
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030117221
DOI: 10.1007/978-3-030-11723-8_16
Abstract
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model local patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. In contrast to these patch-based approaches, we show that deep spatial autoencoding models can be efficiently used to capture normal anatomical variability of entire 2D brain MR slices. A variety of experiments on real MR data containing MS lesions corroborates our hypothesis that we can detect and even delineate anomalies in brain MR images by simply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further improve the segmentation performance over standard deep representation learning.
Involved external institutions
Germany (DE)How to cite
APA:
Baur, C., Wiestler, B., Albarqouni, S., & Navab, N. (2019). Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In Spyridon Bakas, Hugo Kuijf, Mauricio Reyes, Farahani Keyvan, Alessandro Crimi, Theo van Walsum (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 161-169). Granada, ESP: Springer Verlag.
MLA:
Baur, Christoph, et al. "Deep autoencoding models for unsupervised anomaly segmentation in brain MR images." Proceedings of the 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018, Granada, ESP Ed. Spyridon Bakas, Hugo Kuijf, Mauricio Reyes, Farahani Keyvan, Alessandro Crimi, Theo van Walsum, Springer Verlag, 2019. 161-169.
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