Abstract
MRI segmentation is a challenging task due to low anatomical contrast and large inter-patient variation. We propose a feature-driven automatic segmentation framework, combining voxel-wise classification with a Random-Walker (RW) based spatial regularization. Typically, such steps are treated independently, i.e. classification outcome is maximized without taking into account the regularization to follow. Herein we present a method for selective sampling of training patches, in view of the posterior spatial regularization. This aims to concentrate training samples near desired anatomical boundaries, around which the gain from a subsequent RW regularization will potentially be minimal. This trades off a lower classification accuracy for a higher joint segmentation performance. We compare our proposed sampling strategy to conventional uniform sampling on 20 full-body MR T1 scans from the VISCERAL dataset, both with RW and Markov Random Fields regularizations, showing Dice improvements of up to 12\(\times \) with the proposed approach.
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This work was funded by the Swiss National Science Foundation (SNSF) and the Highly Specialized Medicine (HSM) project of Zurich Department of Health.
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Thoma, J., Ozdemir, F., Goksel, O. (2017). Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_8
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DOI: https://doi.org/10.1007/978-3-319-61188-4_8
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