A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
| العنوان: | A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning |
|---|---|
| المؤلفون: | Per Munck af Rosenschöld, Laura Mancini, Mikael Agn, John Ashburner, Anastasia Papadaki, Michael Lundemann, Ian Law, Koen Van Leemput, Steffi Thust, Oula Puonti |
| المصدر: | Medical Image Analysis 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, vol. 54, pp. 220-237 . https://doi.org/10.1016/j.media.2019.03.005Test 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, vol. 54, pp. 220-237 . https://doi.org/10.1016/j.media.2019.03.005Test Med Image Anal |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Organs at Risk, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), medicine.medical_treatment, Boltzmann machine, Computer Science - Computer Vision and Pattern Recognition, Machine Learning (stat.ML), Health Informatics, Article, Machine Learning (cs.LG), 030218 nuclear medicine & medical imaging, Generative probabilistic model, 03 medical and health sciences, 0302 clinical medicine, Statistics - Machine Learning, Glioma, Image Processing, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Radiation treatment planning, Whole-brain segmentation, Restricted Boltzmann machine, Modality (human–computer interaction), Radiological and Ultrasound Technology, Brain Neoplasms, business.industry, Radiotherapy Planning, Computer-Assisted, Pattern recognition, medicine.disease, Magnetic Resonance Imaging, Computer Graphics and Computer-Aided Design, 3. Good health, Radiation therapy, Generative model, Neural Networks, Computer, Computer Vision and Pattern Recognition, Artificial intelligence, Glioblastoma, business, 030217 neurology & neurosurgery |
| الوصف: | 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. corrected one reference |
| وصف الملف: | application/pdf |
| تدمد: | 1361-8415 |
| DOI: | 10.1016/j.media.2019.03.005 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8406e19ce80f180deb68e6dc621ecfb0Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....8406e19ce80f180deb68e6dc621ecfb0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13618415 |
|---|---|
| DOI: | 10.1016/j.media.2019.03.005 |