UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Agn, M; Munck Af Rosenschöld, P; Puonti, O; Lundemann, MJ; Mancini, L; Papadaki, A; Thust, S; ... Van Leemput, K; + view all (2019) A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Medical Image Analysis , 54 pp. 220-237. 10.1016/j.media.2019.03.005. Green open access

[img]
Preview
Text
1-s2.0-S1361841518305103-main.pdf - Published version

Download (4MB) | Preview

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.

Type: Article
Title: A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.media.2019.03.005
Publisher version: https://doi.org/10.1016/j.media.2019.03.005
Language: English
Additional information: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Keywords: Glioma, Whole-brain segmentation, Generative probabilistic model, Restricted Boltzmann machine
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10072998
Downloads since deposit
40Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item