Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist

Erik Thimansson; Erik Baubeta; Jonatan Engman; Anders Bjartell; Sophia Zackrisson

doi:10.1117/1.JMI.11.1.015002

22 February 2024 Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist

Erik Thimansson, Erik Baubeta, Jonatan Engman, Anders Bjartell, Sophia Zackrisson

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 1, 015002 (February 2024). https://doi.org/10.1117/1.JMI.11.1.015002

Abstract

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Erik Thimansson, Erik Baubeta, Jonatan Engman, Anders Bjartell, and Sophia Zackrisson "Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist," Journal of Medical Imaging 11(1), 015002 (22 February 2024). https://doi.org/10.1117/1.JMI.11.1.015002

Received: 16 May 2023; Accepted: 30 January 2024; Published: 22 February 2024

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KEYWORDS

Prostate

Evolutionary algorithms

Magnetic resonance imaging

Image segmentation

Artificial intelligence

Deep learning

Biopsy

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