Clinical evaluation of deep learning and atlas based auto-contouring of bladder and rectum for prostate radiotherapy

Abstract Purpose Auto-contouring may reduce workload, inter-observer variation and time associated with manual contouring of organs at risk (OAR). Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with two auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in prostate cancer patients. Methods Three contouring workflows were defined based on the initial contour generation method including manual (MAN), atlas-based auto-contour (ATLAS) and deep learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on fifteen prostate cancer patients. Then, radiation oncologists (RO) edited each contour while blinded to the manner by which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity and dosimetric evaluation. Results Mean duration for initial contour generation were 10.9 min, 1.4 min and 1.2 min for MAN, DEEP and ATLAS respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP and ATLAS contours were 4.1 min, 4.7 min and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared to MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows. Conclusion Auto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry or clinically relevant dose-volume metrics. This work contributes to growing evidence that deep learning methods are a clinically viable solution for OAR contouring in radiotherapy.