Journal article
Esophagus segmentation from planning CT images using an atlas-based deep learning approach
- Abstract:
-
Background and Objective: One of the main steps in the planning of radiotherapy (RT) is the segmentation of organs at risk (OARs) in Computed Tomography (CT). The esophagus is one of the most difficult OARs to segment. The boundaries between the esophagus and other surrounding tissues are not well-defined, and it is presented in several slices of the CT. Thus, manually segment the esophagus requires a lot of experience and takes time. This difficulty in manual segmentation...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- Elsevier Publisher's website
- Journal:
- Computer Methods and Programs in Biomedicine Journal website
- Volume:
- 197
- Article number:
- 105685
- Publication date:
- 2020-08-07
- Acceptance date:
- 2020-07-28
- DOI:
- ISSN:
-
0169-2607
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1123915
- Local pid:
- pubs:1123915
- Deposit date:
- 2020-08-06
Terms of use
- Copyright holder:
- Elsevier B.V.
- Copyright date:
- 2020
- Rights statement:
- © 2020 Elsevier B.V. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.cmpb.2020.105685
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