High-dose-rate brachytherapy is an accepted standard-of-care treatment for prostate cancer. In this procedure, catheters are inserted using three-dimensional (3D) transrectal ultrasound image-guidance. Their positions are manually segmented for treatment planning and delivery. The transverse ultrasound sweep, which is subject to tip and depth error for catheter localization, is a commonly used ultrasound imaging option available for image acquisition. We propose a two-step pipeline that uses a deep-learning network and curve fitting to automatically localize and model catheters in transversely reconstructed 3D ultrasound images. In the first step, a 3D U-Net was trained to automatically segment all catheters in a 3D ultrasound image. Following this step, curve fitting was implemented to detect the shapes of individual catheters using polynomial fitting. Of the 343 catheters (from 20 patients) in the testing data, the pipeline detected 320 (93.29%) with 7 false positives (2.04%) and 13 false negatives (3.79%). The average distance± one standard deviation between the ground truth and predictions for each catheter shaft was 1.9 ± 0.3 mm. The average difference of each catheter tip was 3.0 ± 0.4 mm. The proposed pipeline provides a method for reducing time spent on verification of catheter positions, minimizing uncertainties, and improving clinical workflow during the procedure. Reducing human variability in catheter placement predictions may increase the accuracy of tracking and radiation dose modelling.
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.
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