Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound
Abstract Purpose Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an essential step in creating such a dataset....
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | BMC Urology |
| Online Access: | https://doi.org/10.1186/s12894-025-01736-4 |
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| Summary: | Abstract Purpose Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an essential step in creating such a dataset. This study presents a comprehensive framework for the registration of 3D transrectal ultrasound (TRUS) to radical prostatectomy specimen (RPS) pathology. Methods The study enrolled patients who underwent 3D TRUS and were scheduled for radical prostatectomy. A four-step process for registering RPS to TRUS was used: image segmentation, 3D reconstruction of RPS pathology, registration and ground truth calculation. Accuracy was assessed using a target-registration error (TRE) based on landmarks visible on both TRUS and pathology. Results 20 Sets of 3D TRUS and RPS pathology were included for analyses. The mean TRE was 3.5 mm, (range: 0.4 to 5.4 mm), with TRE values in the apex-base, left-right and posterior-anterior directions of 2.5 mm, 1.1 mm, and 1.4 mm, respectively. Conclusion The framework proposed in this study accomplishes precise registration between prostate pathology and imaging. The methodologies employed hold the potential for broader application across diverse imaging modalities and other target organs. However, limitations such a small sample size and the need for manual segmentation should be considered when interpreting te results. Future efforts should focus on automating key steps to enhance reproducibility and scalability. |
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| ISSN: | 1471-2490 |