Physics and imaging in radiation oncology · 2026
Automatic segmentation of the urethra and prostate zones with deep learning on T2-weighted magnetic resonance imaging.
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Holmlund William, Simkó Attila, Söderkvist Karin, Palásti Péter, Tótin Szilvia, Kalmár Kamilla, Domoki Zsófia, Fejes Zsuzsanna, Kincses Tamás Z, Brynolfsson Patrik, Nyholm Tufve
Аннотация
BACKGROUND AND PURPOSE: Accurate segmentation of the urethra is crucial for safe focal dose escalated radiotherapy, while prostate zone identification is important for prostate cancer diagnosis. Manual delineations on magnetic resonance imaging (MRI) are labour-intensive and variable, and while deep learning offers promise in automating this process, no available solution currently exists. This study aimed to develop and evaluate a deep learning model for automatic segmentation of the urethra, prostate and all prostate zones and benchmark its performance against inter-reader variability and assess generalisability to external data from a different MRI vendor.
MATERIALS AND METHODS: The public datasets ProstateZones and PROSTATEx included 200 magnetic resonance images with manual delineations, with 160 used for training/validation and 40 with independent duplicate segmentations used as a test set. A nnU-Net deep learning model was evaluated on the unseen test set and externally validated on a dataset with 55 samples. Performance was assessed using Dice Similarity Coefficient (DSC), Surface DSC, percentile Symmetric Surface Distance, and Center Line Distance (CLD) metrics.
RESULTS: The model outperformed the inter-reader variability on multiple structures, and notably on all metrics for the urethra, with median CLD values of 2.8 and 2.9 mm compared to 3.6 mm for inter-reader variability. External validation showed robust generalisability to a dataset collected from a different vendor.
CONCLUSIONS: This study demonstrated that a deep learning model can achieve expert-level performance in automated segmentation of the urethra, prostate, and prostate zones. Robust performance on external data highlighted potential as a decision support solution.
MATERIALS AND METHODS: The public datasets ProstateZones and PROSTATEx included 200 magnetic resonance images with manual delineations, with 160 used for training/validation and 40 with independent duplicate segmentations used as a test set. A nnU-Net deep learning model was evaluated on the unseen test set and externally validated on a dataset with 55 samples. Performance was assessed using Dice Similarity Coefficient (DSC), Surface DSC, percentile Symmetric Surface Distance, and Center Line Distance (CLD) metrics.
RESULTS: The model outperformed the inter-reader variability on multiple structures, and notably on all metrics for the urethra, with median CLD values of 2.8 and 2.9 mm compared to 3.6 mm for inter-reader variability. External validation showed robust generalisability to a dataset collected from a different vendor.
CONCLUSIONS: This study demonstrated that a deep learning model can achieve expert-level performance in automated segmentation of the urethra, prostate, and prostate zones. Robust performance on external data highlighted potential as a decision support solution.