Male Pelvic Multi-Organ Segmentation On Transrectal Ultrasound Using Anchor-Free Mask Cnn

Purpose: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning-based method for male pelvic multi-organ segmentation on transrectal ultrasound images.Methods: We developed an anchor-free mask convolutional neural network (CNN) that consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS), and a mask head. The backbone extracts multi-level and multi-scale features from an ultrasound (US) image. The FOCS utilizes these features to detect and label (classify) the volume-of-interests (VOIs) of organs. In contrast to the design of a previously investigated mask regional CNN (Mask R-CNN), the FCOS is anchor-free, which can capture the spatial correlation of multiple organs. The mask head performs segmentation on each detected VOI, where a spatial attention strategy is integrated into the mask head to focus on informative feature elements and suppress noise. For evaluation, we retrospectively investigated 83 prostate cancer patients by fivefold cross-validation and a hold-out test. The prostate, bladder, rectum, and urethra were segmented and compared with manual contours using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), mean surface distance (MSD), center of mass distance (CMD), and volume difference (VD).Results: The proposed method visually outperforms two competing methods, showing better agreement with manual contours and fewer misidentified speckles. In the cross-validation study, the respective DSC and HD95 results were as follows for each organ: bladder 0.75 +/- 0.12, 2.58 +/- 0.7 mm; prostate 0.93 +/- 0.03, 2.28 +/- 0.64 mm; rectum 0.90 +/- 0.07, 1.65 +/- 0.52 mm; and urethra 0.86 +/- 0.07, 1.85 +/- 1.71 mm. For the hold-out tests, the DSC and HD95 results were as follows: bladder 0.76 +/- 0.13, 2.93 +/- 1.29 mm; prostate 0.94 +/- 0.03, 2.27 +/- 0.79 mm; rectum 0.92 +/- 0.03, 1.90 +/- 0.28 mm; and urethra 0.85 +/- 0.06, 1.81 +/- 0.72 mm. Segmentation was performed in under 5 seconds.Conclusion: The proposed method demonstrated fast and accurate multi-organ segmentation performance. It can expedite the contouring step of prostate brachytherapy and potentially enable autoplanning and auto-evaluation. (C) 2021 American Association of Physicists in Medicine