A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients
(2016) Medical Imaging 2016: Image Processing In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9784.- Abstract
We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the... (More)
We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method.
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- author
- Agn, Mikael
; Law, Ian
; Munck Af Rosenschöld, Per
LU
and Van Leemput, Koen
- publishing date
- 2016-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Medical Imaging 2016 : Image Processing - Image Processing
- series title
- Progress in Biomedical Optics and Imaging - Proceedings of SPIE
- editor
- Styner, Martin A. and Angelini, Elsa D.
- volume
- 9784
- article number
- 97841D
- publisher
- SPIE
- conference name
- Medical Imaging 2016: Image Processing
- conference location
- San Diego, United States
- conference dates
- 2016-03-01 - 2016-03-03
- external identifiers
-
- scopus:84981710043
- ISSN
- 1605-7422
- ISBN
- 9781510600195
- DOI
- 10.1117/12.2216814
- language
- English
- LU publication?
- no
- id
- 872a7f24-4ae5-4189-87fd-4ada085f1d53
- date added to LUP
- 2020-07-28 09:04:09
- date last changed
- 2023-07-20 08:31:44
@inproceedings{872a7f24-4ae5-4189-87fd-4ada085f1d53, abstract = {{<p>We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method.</p>}}, author = {{Agn, Mikael and Law, Ian and Munck Af Rosenschöld, Per and Van Leemput, Koen}}, booktitle = {{Medical Imaging 2016 : Image Processing}}, editor = {{Styner, Martin A. and Angelini, Elsa D.}}, isbn = {{9781510600195}}, issn = {{1605-7422}}, language = {{eng}}, month = {{01}}, publisher = {{SPIE}}, series = {{Progress in Biomedical Optics and Imaging - Proceedings of SPIE}}, title = {{A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients}}, url = {{http://dx.doi.org/10.1117/12.2216814}}, doi = {{10.1117/12.2216814}}, volume = {{9784}}, year = {{2016}}, }