Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning.
adaptive radiotherapy
contour propagation
deep learning
deformable image registration
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Apr 2021
Apr 2021
Historique:
revised:
13
01
2021
received:
03
10
2020
accepted:
23
01
2021
pubmed:
6
2
2021
medline:
15
5
2021
entrez:
5
2
2021
Statut:
ppublish
Résumé
To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1764-1770Subventions
Organisme : NIH HHS
ID : 1R01 CA176553
Pays : United States
Organisme : NIH HHS
ID : R01CA227713
Pays : United States
Informations de copyright
© 2021 American Association of Physicists in Medicine.
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