StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
release_evcwrrzdxnaqvlymzgzqhwptpa
by
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Pavan Tummala, Shubham Kumar Agrawal, Aishwarya Jauhari, Aman Kalra, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger
2022
Abstract
Expert interpretation of anatomical images of the human brain is the central
part of neuro-radiology. Several machine learning-based techniques have been
proposed to assist in the analysis process. However, the ML models typically
need to be trained to perform a specific task, e.g., brain tumour segmentation
or classification. Not only do the corresponding training data require
laborious manual annotations, but a wide variety of abnormalities can be
present in a human brain MRI - even more than one simultaneously, which renders
representation of all possible anomalies very challenging. Hence, a possible
solution is an unsupervised anomaly detection (UAD) system that can learn a
data distribution from an unlabelled dataset of healthy subjects and then be
applied to detect out of distribution samples. Such a technique can then be
used to detect anomalies - lesions or abnormalities, for example, brain
tumours, without explicitly training the model for that specific pathology.
Several Variational Autoencoder (VAE) based techniques have been proposed in
the past for this task. Even though they perform very well on controlled
artificially simulated anomalies, many of them perform poorly while detecting
anomalies in clinical data. This research proposes a compact version of the
"context-encoding" VAE (ceVAE) model, combined with pre and post-processing
steps, creating a UAD pipeline (StRegA), which is more robust on clinical data,
and shows its applicability in detecting anomalies such as tumours in brain
MRIs. The proposed pipeline achieved a Dice score of 0.642±0.101 while
detecting tumours in T2w images of the BraTS dataset and 0.859±0.112 while
detecting artificially induced anomalies, while the best performing baseline
achieved 0.522±0.135 and 0.783±0.111, respectively.
In text/plain
format
Archived Files and Locations
application/pdf 2.1 MB
file_qb5j6d5qb5eencoutpyoviyv44
|
arxiv.org (repository) web.archive.org (webarchive) |
2201.13271v1
access all versions, variants, and formats of this works (eg, pre-prints)