Ensemble learning and tensor regularization for cone-beam computed tomography-based pelvic organ segmentation

Cone-beam computed tomography (CBCT) is a widely accessible low-dose imaging approach compatible with on-table patient anatomy observation for radiotherapy. However, its use in comprehensive anatomy monitoring is hindered by low contrast and low signal-to-noise ratio and a large presence of artifacts, resulting in difficulty in identifying organ and structure boundaries either manually or automatically. In this study, we propose and develop an ensemble deep-learning model to segment post-prostatectomy organs automatically.

We utilize the ensemble logic in various modules during the segmentation process to alleviate the impact of low image quality of CBCT. Specifically, (1) semantic attention was obtained from an ensemble 2.5D You-only-look-once detector to consistently define regions of interest, (2) multiple view-specific two-stream 2.5D segmentation networks were developed, using auxiliary high-quality CT data to aid CBCT segmentation, and (3) a novel tensor-regularized ensemble scheme was proposed to aggregate the estimates from multiple views and regularize the spatial integrity of the final segmentation.

A cross-validation study achieved Dice similarity coefficient and mean surface distance of 0.779 $$ 0.069 and 2.895 $$ 1.496 mm for the rectum, and 0.915 $$ 0.055 and 1.675 $$ 1.311 mm for the bladder.

The proposed ensemble scheme manages to enhance the geometric integrity and robustness of the contours derived from CBCT with light network components. The tensor regularization approach generates organ results conforming to anatomy and physiology, without compromising typical quantitative performance in Dice similarity coefficient and mean surface distance, to support further clinical interpretation and decision making.