Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning. Issue 4 (1st March 2021) - Liang, Xiaokun
Abstract : Purpose: 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). Methods: 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. Results: 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. Conclusions: 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.