Automatic Prostate Volume Estimation in Transabdominal Ultrasound Images

TL;DR

This paper presents a deep learning framework for automatic prostate volume estimation from transabdominal ultrasound images, aiming to provide a non-invasive, accurate alternative to traditional methods for early prostate cancer detection.

Contribution

It introduces a novel deep learning-based method for prostate segmentation and volume estimation in TAUS images, addressing challenges of image quality and operator dependence.

Findings

Achieved a mean volumetric error of -5.5 mL in prostate volume estimation.

Demonstrated an average relative error between 5% and 15%.

Showed promising potential for non-invasive prostate cancer risk stratification.

Abstract

Prostate cancer is a leading health concern among men, requiring accurate and accessible methods for early detection and risk stratification. Prostate volume (PV) is a key parameter in multivariate risk stratification for early prostate cancer detection, commonly estimated using transrectal ultrasound (TRUS). While TRUS provides precise prostate volume measurements, its invasive nature often compromises patient comfort. Transabdominal ultrasound (TAUS) provides a non-invasive alternative but faces challenges such as lower image quality, complex interpretation, and reliance on operator expertise. This study introduces a new deep-learning-based framework for automatic PV estimation using TAUS, emphasizing its potential to enable accurate and non-invasive prostate cancer risk stratification. A dataset of TAUS videos from 100 individual patients was curated, with manually delineated prostate boundaries and calculated diameters by an expert clinician as ground truth. The introduced framework integrates deep-learning models for prostate segmentation in both axial and sagittal planes, automatic prostate diameter estimation, and PV calculation. Segmentation performance was evaluated using Dice correlation coefficient (%) and Hausdorff distance (mm). Framework's volume estimation capabilities were evaluated on volumetric error (mL). The framework demonstrates that it can estimate PV from TAUS videos with a mean volumetric error of -5.5 mL, which results in an average relative error between 5 and 15%. The introduced framework for automatic PV estimation from TAUS images, utilizing deep learning models for prostate segmentation, shows promising results. It effectively segments the prostate and estimates its volume, offering potential for reliable, non-invasive risk stratification for early prostate detection.