A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs
(2021) European Society Radiation Oncology 2021 In Radiotherapy and Oncology 161(Suppl 1). p.1417-1418- Abstract
- Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel,... (More)
- Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel, large bowel, rectum, and bladder). (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/33ec4dae-b7e8-4737-a943-f6b363eae7e8
- author
- Lempart, Michael LU ; Nilsson, Martin LU ; Scherman, Jonas ; Nilsson, Mikael LU ; Jamtheim Gustafsson, Christian LU ; Munck Af Rosenschöld, Per LU and Olsson, Lars E LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Deep Learning, Radiation therapy, Semantic segmentation, Anal cancer
- in
- Radiotherapy and Oncology
- volume
- 161
- issue
- Suppl 1
- article number
- PO-1691
- pages
- 1417 - 1418
- publisher
- Elsevier
- conference name
- European Society Radiation Oncology 2021
- conference location
- Madrid, Spain
- conference dates
- 2021-08-26 - 2021-08-31
- ISSN
- 1879-0887
- DOI
- 10.1016/S0167-8140(21)08142-1
- language
- English
- LU publication?
- yes
- id
- 33ec4dae-b7e8-4737-a943-f6b363eae7e8
- date added to LUP
- 2021-10-28 10:06:31
- date last changed
- 2023-07-20 03:08:01
@misc{33ec4dae-b7e8-4737-a943-f6b363eae7e8, abstract = {{Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel, large bowel, rectum, and bladder).}}, author = {{Lempart, Michael and Nilsson, Martin and Scherman, Jonas and Nilsson, Mikael and Jamtheim Gustafsson, Christian and Munck Af Rosenschöld, Per and Olsson, Lars E}}, issn = {{1879-0887}}, keywords = {{Deep Learning; Radiation therapy; Semantic segmentation; Anal cancer}}, language = {{eng}}, note = {{Conference Abstract}}, number = {{Suppl 1}}, pages = {{1417--1418}}, publisher = {{Elsevier}}, series = {{Radiotherapy and Oncology}}, title = {{A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs}}, url = {{http://dx.doi.org/10.1016/S0167-8140(21)08142-1}}, doi = {{10.1016/S0167-8140(21)08142-1}}, volume = {{161}}, year = {{2021}}, }