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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. / Agn, Mikael; Munck Af Rosenschöld, Per; Puonti, Oula; Lundemann, Michael J; Mancini, Laura; Papadaki, Anastasia; Thust, Steffi; Ashburner, John; Law, Ian; Van Leemput, Koen.

In: Medical Image Analysis, Vol. 54, 05.2019, p. 220-237.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Agn, M, Munck Af Rosenschöld, P, Puonti, O, Lundemann, MJ, Mancini, L, Papadaki, A, Thust, S, Ashburner, J, Law, I & Van Leemput, K 2019, 'A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning', Medical Image Analysis, vol. 54, pp. 220-237. https://doi.org/10.1016/j.media.2019.03.005

APA

Agn, M., Munck Af Rosenschöld, P., Puonti, O., Lundemann, M. J., Mancini, L., Papadaki, A., Thust, S., Ashburner, J., Law, I., & Van Leemput, K. (2019). A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Medical Image Analysis, 54, 220-237. https://doi.org/10.1016/j.media.2019.03.005

Vancouver

Agn M, Munck Af Rosenschöld P, Puonti O, Lundemann MJ, Mancini L, Papadaki A et al. A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Medical Image Analysis. 2019 May;54:220-237. https://doi.org/10.1016/j.media.2019.03.005

Author

Agn, Mikael ; Munck Af Rosenschöld, Per ; Puonti, Oula ; Lundemann, Michael J ; Mancini, Laura ; Papadaki, Anastasia ; Thust, Steffi ; Ashburner, John ; Law, Ian ; Van Leemput, Koen. / A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. In: Medical Image Analysis. 2019 ; Vol. 54. pp. 220-237.

Bibtex

@article{e2597657e9de4a6fa0c56015b8d74b2d,

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

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.",

author = "Mikael Agn and {Munck Af Rosensch{\"o}ld}, Per and Oula Puonti and Lundemann, {Michael J} and Laura Mancini and Anastasia Papadaki and Steffi Thust and John Ashburner and Ian Law and {Van Leemput}, Koen",

year = "2019",

month = may,

doi = "10.1016/j.media.2019.03.005",

language = "English",

volume = "54",

pages = "220--237",

journal = "Medical Image Analysis",

issn = "1361-8415",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

AU - Agn, Mikael

AU - Munck Af Rosenschöld, Per

AU - Puonti, Oula

AU - Lundemann, Michael J

AU - Mancini, Laura

AU - Papadaki, Anastasia

AU - Thust, Steffi

AU - Ashburner, John

AU - Law, Ian

AU - Van Leemput, Koen

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

U2 - 10.1016/j.media.2019.03.005

DO - 10.1016/j.media.2019.03.005

M3 - Journal article

C2 - 30952038

VL - 54

SP - 220

EP - 237

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

ER -

ID: 235917192

Forskning