A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
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A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. / Agn, Mikael; Munck Af Rosenschöld, Per; Puonti, Oula; Lundemann, Michael J; Mancini, Laura; Papadaki, Anastasia; Thust, Steffi; Ashburner, John; Law, Ian; Van Leemput, Koen.
In: Medical Image Analysis, Vol. 54, 05.2019, p. 220-237.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
AU - Agn, Mikael
AU - Munck Af Rosenschöld, Per
AU - Puonti, Oula
AU - Lundemann, Michael J
AU - Mancini, Laura
AU - Papadaki, Anastasia
AU - Thust, Steffi
AU - Ashburner, John
AU - Law, Ian
AU - Van Leemput, Koen
N1 - Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
U2 - 10.1016/j.media.2019.03.005
DO - 10.1016/j.media.2019.03.005
M3 - Journal article
C2 - 30952038
VL - 54
SP - 220
EP - 237
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
ER -
ID: 235917192