Sophia Zackrisson
Research group manager, Principal investigator, Professor, MD
Deep learning performance on MRI prostate gland segmentation : evaluation of two commercially available algorithms compared with an expert radiologist
Author
Summary, in English
PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.
APPROACH: We included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food and Drug Association (FDA)-cleared and CE-marked algorithms (DLA1 and DLA2) using an expert radiologist's manual contours as a reference standard (RSexp) in this clinical heterogeneous MRI dataset. No in-house training of the DLAs was performed before testing. Several methods for comparing segmentation overlap were used, the Dice similarity coefficient (DSC) being the most important.
RESULTS: The DSC mean and standard deviation for DLA1 versus the radiologist reference standard (RSexp) was 0.90±0.05 and for DLA2 versus RSexp it was 0.89±0.04. A paired t-test to compare the DSC for DLA1 and DLA2 showed no statistically significant difference (p=0.8).
CONCLUSIONS: Two commercially available DL algorithms (FDA-cleared and CE-marked) can perform accurate whole-gland prostate segmentation on a par with expert radiologist manual planimetry on a real-world clinical dataset. Implementing AI models in the clinical routine may free up time that can be better invested in complex work tasks, adding more patient value.
Department/s
- LUCC: Lund University Cancer Centre
- Radiology Diagnostics, Malmö
- eSSENCE: The e-Science Collaboration
- Urological cancer, Malmö
- EpiHealth: Epidemiology for Health
- LU Profile Area: Light and Materials
- LTH Profile Area: Photon Science and Technology
Publishing year
2024
Language
English
Publication/Series
Journal of Medical Imaging
Volume
11
Issue
1
Document type
Journal article
Publisher
SPIE
Topic
- Medical Image Processing
- Radiology, Nuclear Medicine and Medical Imaging
Status
Published
Research group
- Radiology Diagnostics, Malmö
- Urological cancer, Malmö
ISBN/ISSN/Other
- ISSN: 2329-4302