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Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker

Janine Thoma, Firat Ozdemir, Orcun Goksel
Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers
Athens, Greece 2016

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 toconventional 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× with the proposed approach.


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@InProceedings{eth_biwi_01323,
  author = {Janine Thoma and Firat Ozdemir and Orcun Goksel},
  title = {Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker},
  booktitle = {Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers},
  year = {2016},
  pages = {83--93},
  series = {Lecture Notes in Computer Science (LNCS)},
  publisher = {Springer International Publishing},
  keywords = {}
}