Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning - Lucía Cubero; Laura García-Elcano; Eugenia Mylona; Adrien Boue-Rafle; Cesare Cozzarini; Maria Giulia Ubeira Gabellini; Tiziana Rancati; Claudio Fiorino; Renaud de Crevoisier; Oscar Acosta; Javier Pascau (Physics and Imaging in Radiation Oncology, 2023)
Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours. Materials and methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.