A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

Mikael Agn*, Per Munck af Rosenschöld, Oula Puonti, Michael J. Lundemann, Laura Mancini, Anastasia Papadaki, Steffi Thust, John Ashburner, Ian Law, Koen Van Leemput

*Corresponding author for this work

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Abstract

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

Original languageEnglish
JournalMedical Image Analysis
Volume54
Pages (from-to)220-237
ISSN1361-8415
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Generative probabilistic model
  • Glioma
  • Restricted Boltzmann machine
  • Whole-brain segmentation

Cite this

Agn, M., Munck af Rosenschöld, P., Puonti, O., Lundemann, M. J., Mancini, L., Papadaki, A., Thust, S., Ashburner, J., Law, I., & Van Leemput, K. (2019). A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Medical Image Analysis, 54, 220-237. https://doi.org/10.1016/j.media.2019.03.005