Automated catheter segmentation using 3D ultrasound images in high-dose-rate prostate brachytherapy

PURPOSE: High-dose-rate brachytherapy (HDR-BT) is an important treatment modality for prostate cancer that maximizes radiation dose to cancerous tissue while sparing surrounding organs. Currently, treatment planning during HDR-BT is manually completed by medical physicists, a time-consuming and observer dependent process. We propose using deep learning through a U-Net architecture to automatically segment catheters in HDR prostate brachytherapy treatment planning. METHODS: 3D Ultrasound data along with the corresponding manual contours were obtained from 49 patients undergoing HDR prostate brachytherapy. The dataset was preprocessed and then exported for training and evaluation. The resulting model was assessed both quantitatively with binary segmentation metrics and qualitatively through 3D reconstructions. RESULTS: The output segmentations demonstrated consistency on different patient datasets and good visual agreement with ground truth images. The average execution time per patient is under 30.0 s, a significant improvement from manual contouring, which may require upwards of an hour. CONCLUSION: We trained and evaluated a 3D U-Net model for automatic catheter segmentation on 3D transrectal ultrasound images generated through HDR prostate brachytherapy. Deep learning methods such as the 3D U-Net used in this scenario appear to be a promising method for automatic catheter segmentation in prostate brachytherapy.