Related Experiment Video
Updated: Jul 30, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Development and acceptability validation of a deep learning-based tool for whole-prostate segmentation on
Lili Xu1,2, Gumuyang Zhang1, Daming Zhang1
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Background:
Accurate whole prostate segmentation on magnetic resonance imaging (MRI) is important in the management of prostatic diseases. In this multicenter study, we aimed to develop and evaluate a clinically applicable deep learning-based tool for automatic whole prostate segmentation on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI).
Methods:
In this retrospective study, 3-dimensional (3D) U-Net-based models in the segmentation tool were trained with 223 patients who underwent prostate MRI and subsequent biopsy from 1 hospital and validated in 1 internal testing cohort (n=95) and 3 external testing cohorts: PROSTATEx Challenge for T2WI and DWI (n=141), Tongji Hospital (n=30), and Beijing Hospital for T2WI (n=29). Patients from the latter 2 centers were diagnosed with advanced prostate cancer. The DWI model was further fine-tuned to compensate for the scanner variety in external testing. A quantitative evaluation, including Dice similarity coefficients (DSCs), 95% Hausdorff distance (95HD), and average boundary distance (ABD), and a qualitative analysis were used to evaluate the clinical usefulness.
Results:
The segmentation tool showed good performance in the testing cohorts on T2WI (DSC: 0.922 for internal testing and 0.897-0.947 for external testing) and DWI (DSC: 0.914 for internal testing and 0.815 for external testing with fine-tuning). The fine-tuning process significantly improved the DWI model's performance in the external testing dataset (DSC: 0.275 vs. 0.815; P<0.01). Across all testing cohorts, the 95HD was <8 mm, and the ABD was <3 mm. The DSCs in the prostate midgland (T2WI: 0.949-0.976; DWI: 0.843-0.942) were significantly higher than those in the apex (T2WI: 0.833-0.926; DWI: 0.755-0.821) and base (T2WI: 0.851-0.922; DWI: 0.810-0.929) (all P values <0.01). The qualitative analysis showed that 98.6% of T2WI and 72.3% of DWI autosegmentation results in the external testing cohort were clinically acceptable.
Conclusions:
The 3D U-Net-based segmentation tool can automatically segment the prostate on T2WI with good and robust performance, especially in the prostate midgland. Segmentation on DWI was feasible, but fine-tuning might be needed for different scanners.
