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A modality-adaptive...
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
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- Agn, Mikael (författare)
- Technical University of Denmark
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- Munck af Rosenschöld, Per (författare)
- Lund University,Lunds universitet,Medicinsk strålningsfysik, Malmö,Forskargrupper vid Lunds universitet,Medical Radiation Physics, Malmö,Lund University Research Groups,Skåne University Hospital
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- Puonti, Oula (författare)
- Hvidovre Hospital
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- Lundemann, Michael J. (författare)
- Copenhagen University Hospital
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- Mancini, Laura (författare)
- University College London,Great Ormond Street Hospital
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- Papadaki, Anastasia (författare)
- Great Ormond Street Hospital,University College London
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- Thust, Steffi (författare)
- Great Ormond Street Hospital,University College London
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- Ashburner, John (författare)
- University College London
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- Law, Ian (författare)
- Copenhagen University Hospital
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- Van Leemput, Koen (författare)
- Massachusetts General Hospital,Technical University of Denmark
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(creator_code:org_t)
- Elsevier BV, 2019
- 2019
- Engelska 18 s.
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 54, s. 220-237
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Generative probabilistic model
- Glioma
- Restricted Boltzmann machine
- Whole-brain segmentation
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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