Leveraging 3D Information in Unsupervised Brain MRI Segmentation
- URL: http://arxiv.org/abs/2101.10674v1
- Date: Tue, 26 Jan 2021 10:04:57 GMT
- Title: Leveraging 3D Information in Unsupervised Brain MRI Segmentation
- Authors: Benjamin Lambert, Maxime Louis, Senan Doyle, Florence Forbes, Michel
Dojat, Alan Tucholka
- Abstract summary: Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a Variational Autoencoder (VAE)
Here, we propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs.
As a side contribution, we present a new loss function guarantying a robust training. Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions.
- Score: 1.6148039130053087
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automatic segmentation of brain abnormalities is challenging, as they vary
considerably from one pathology to another. Current methods are supervised and
require numerous annotated images for each pathology, a strenuous task. To
tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are
proposed, detecting anomalies as outliers of a healthy model learned using a
Variational Autoencoder (VAE). Previous work on UAD adopted a 2D approach,
meaning that MRIs are processed as a collection of independent slices. Yet, it
does not fully exploit the spatial information contained in MRI. Here, we
propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side
contribution, we present a new loss function guarantying a robust training.
Learning is performed using a multicentric dataset of healthy brain MRIs, and
segmentation performances are estimated on White-Matter Hyperintensities and
tumors lesions. Experiments demonstrate the interest of 3D methods which
outperform their 2D counterparts.
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