A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
Agn, Mikael ; Munck af Rosenschöld, Per LU
; Puonti, Oula
; Lundemann, Michael J.
; Mancini, Laura
; Papadaki, Anastasia
; Thust, Steffi
; Ashburner, John
; Law, Ian
and Van Leemput, Koen
(2019)
In Medical Image Analysis
54.
p.220-237
- 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.... (More)
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.
(Less)
- author
- Agn, Mikael
; Munck af Rosenschöld, Per
LU
; Puonti, Oula
; Lundemann, Michael J.
; Mancini, Laura
; Papadaki, Anastasia
; Thust, Steffi
; Ashburner, John
; Law, Ian
and Van Leemput, Koen
- organization
- publishing date
- 2019-05-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Generative probabilistic model, Glioma, Restricted Boltzmann machine, Whole-brain segmentation
- in
- Medical Image Analysis
- volume
- 54
- pages
- 18 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:30952038
- scopus:85063664420
- ISSN
- 1361-8415
- DOI
- 10.1016/j.media.2019.03.005
- language
- English
- LU publication?
- yes
- id
- a7d19e6e-7e8b-462f-905f-db9b576592dc
- date added to LUP
- 2020-07-28 08:36:51
- date last changed
- 2024-04-03 11:55:02
@article{a7d19e6e-7e8b-462f-905f-db9b576592dc,
abstract = {{<p>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.</p>}},
author = {{Agn, Mikael and Munck af Rosenschöld, Per and Puonti, Oula and Lundemann, Michael J. and Mancini, Laura and Papadaki, Anastasia and Thust, Steffi and Ashburner, John and Law, Ian and Van Leemput, Koen}},
issn = {{1361-8415}},
keywords = {{Generative probabilistic model; Glioma; Restricted Boltzmann machine; Whole-brain segmentation}},
language = {{eng}},
month = {{05}},
pages = {{220--237}},
publisher = {{Elsevier}},
series = {{Medical Image Analysis}},
title = {{A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning}},
url = {{http://dx.doi.org/10.1016/j.media.2019.03.005}},
doi = {{10.1016/j.media.2019.03.005}},
volume = {{54}},
year = {{2019}},
}