A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

Mikael Agn; Ian Law; Per Munck Af Rosenschöld; Koen Van Leemput

doi:10.1117/12.2216814

A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

Mikael Agn, Ian Law, Per Munck Af Rosenschöld, Koen Van Leemput

Copenhagen University Hospital Herlev and Gentofte

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 method.

Original language	English
Title of host publication	SPIE Medical Imaging 2016: Image Processing
Number of pages	9
Volume	9784
Publisher	SPIE - International Society for Optical Engineering
Publication date	2016
Article number	97841D
DOIs	https://doi.org/10.1117/12.2216814
Publication status	Published - 2016
Event	SPIE Medical Imaging 2016 - Town & Country Resort and Convention Cente, San Diego, United States Duration: 27 Feb 2016 → 3 Mar 2016 https://spie.org/conferences-and-exhibitions/past-conferences-and-exhibitions/medical-imaging-2016

Conference

Conference	SPIE Medical Imaging 2016
Location	Town & Country Resort and Convention Cente
Country/Territory	United States
City	San Diego
Period	27/02/2016 → 03/03/2016
Internet address	https://spie.org/conferences-and-exhibitions/past-conferences-and-exhibitions/medical-imaging-2016

Series	Proceedings of SPIE - The International Society for Optical Engineering
ISSN	0277-786X

Bibliographical note

Copyright 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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Agn, Mikael ; Law, Ian ; Munck Af Rosenschöld, Per et al. / A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients. SPIE Medical Imaging 2016: Image Processing. Vol. 9784 SPIE - International Society for Optical Engineering, 2016. (Proceedings of SPIE - The International Society for Optical Engineering).

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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 method.",

author = "Mikael Agn and Ian Law and {Munck Af Rosensch{\"o}ld}, Per and {Van Leemput}, Koen",

note = "Copyright 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.; SPIE Medical Imaging 2016 ; Conference date: 27-02-2016 Through 03-03-2016",

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volume = "9784",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE - International Society for Optical Engineering",

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Agn, M, Law, I, Munck Af Rosenschöld, P & Van Leemput, K 2016, A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients. in SPIE Medical Imaging 2016: Image Processing. vol. 9784, 97841D, SPIE - International Society for Optical Engineering, Proceedings of SPIE - The International Society for Optical Engineering, SPIE Medical Imaging 2016, San Diego, California, United States, 27/02/2016. https://doi.org/10.1117/12.2216814

A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients. / Agn, Mikael; Law, Ian; Munck Af Rosenschöld, Per et al.
SPIE Medical Imaging 2016: Image Processing. Vol. 9784 SPIE - International Society for Optical Engineering, 2016. 97841D (Proceedings of SPIE - The International Society for Optical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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N1 - Copyright 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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AB - 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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Agn M, Law I, Munck Af Rosenschöld P, Van Leemput K. A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients. In SPIE Medical Imaging 2016: Image Processing. Vol. 9784. SPIE - International Society for Optical Engineering. 2016. 97841D. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2216814

A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

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