Automatic prostate volume estimation in transabdominal ultrasound images.

Abstract

RESULTS

The axial model outperformed the sagittal model, achieving a Dice score of 0.76 ± 0.16 versus 0.68 ± 0.21, a Dice-MidPlane of 0.91 ± 0.06 versus 0.83. ± 0.10, and a Hausdorff distance of 6.21 ± 4.33 mm versus 7.93 ± 4.27 mm. The framework estimated PV with a mean volumetric error of -2.1 mL (95 % limits of agreement: -16.9 to 21.1 mL), resulting in a relative error smaller than 25 %.

METHODS

A dataset of TAUS videos from 100 patients (median age 67, 95-percentile range 55-81.2) was curated, with expert-delineated prostate boundaries and diameter calculations as ground truth. The framework integrates deep-learning models for prostate segmentation in both axial and sagittal planes, automatic diameter estimation, and PV calculation. Segmentation performance was evaluated using Dice correlation coefficient (%) and Hausdorff distance (mm), while volume estimation accuracy was assessed through volumetric error (mL).

INTRODUCTION

Prostate cancer is a major health concern requiring accurate and accessible methods for early detection and risk stratification. Prostate volume (PV) is a key parameter in multivariate risk assessment, traditionally measured using transrectal ultrasound (TRUS). While TRUS provides precise measurements, its invasive nature affects patient comfort. Transabdominal ultrasound (TAUS) offers a non-invasive alternative but is limited by lower image quality and operator dependence. This study presents a deep-learning-based framework for automatic PV estimation using TAUS, aiming to improve non-invasive prostate cancer risk stratification.

CONCLUSION

These findings highlight the potential of deep learning for accurate, non-invasive PV estimation, supporting improved prostate cancer risk assessment.