Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy

• Published in: Diagnostics (Basel) Vol. 15; no. 9; p. 1141
• Main Authors: Boellaard, Thierry N., van Erck, Roy, van der Graaf, Sophia H., de Boer, Lisanne, van der Poel, Henk G., Mertens, Laura S., van Leeuwen, Pim J., Dashtbozorg, Behdad
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.04.2025; MDPI
• Subjects: Accuracy; Algorithms; artificial intelligence; Automation; Biopsy; Comparative studies; Magnetic resonance imaging; magnetic resonance imaging (MRI); Performance evaluation; Prostate cancer; prostatectomy; prostatic neoplasms; Software; Surgery; Three dimensional imaging; three-dimensional images; tumor segmentation; Netherlands; Germany; magnetic resonance imaging (MRI); prostate segmentation; prostatic neoplasms; three-dimensional images; tumor segmentation; prostatectomy; artificial intelligence
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15091141

Background: Robot-assisted radical prostatectomy (RARP) is a common treatment option for prostate cancer. A 3D model for surgical guidance can improve surgical outcomes. Manual expert radiologist segmentation of the prostate and tumor in prostate MRI to create 3D models is labor-intensive and prone to inter-observer variability, highlighting the need for automated segmentation methods. Methods: This study evaluates the performance of the prostate and tumor segmentation using a commercially available AI tool without (fully automated) and with manual adjustment (AI-assisted) compared to manual segmentations on 120 patients, using several metrics, including Dice Coefficient and Hausdorff distance. Tumor detection rates were assessed with recall and precision. Results: For the prostate, both the fully automated AI model and AI-assisted model achieved a mean Dice score of 0.88, while AI-assisted had a lower Hausdorff distance (7.22 mm) compared to the fully automated (7.40 mm). For tumor segmentations, the Dice scores were 0.53 and 0.62, with Hausdorff distances of 9.53 mm and 6.62 mm obtained for fully automated AI and AI-assisted methods, respectively. The fully automated AI method had a recall of 0.74 and a precision of 0.76 in tumor detection, while the AI-assisted method achieved 0.95 recall and 0.94 precision. Fully automated segmentation required less than 1 min, while adjustments for the AI-assisted segmentation took an additional 81 s, and manual segmentation took approximately 15–30 min. Conclusions: The fully automated AI model shows promising results, offering high tumor detection rates and acceptable segmentation metrics. The AI-assisted strategy improved the relevant metrics with minimal additional time investment. Therefore, the AI-assisted segmentation method is promising for allowing 3D-model-guided surgery for all patients undergoing RARP.