Open Access

Benign prostatic hyperplasia (BPH) is one of the most widespread diseases among men older than 50 years. The literature provides various cut-off values for pathological enlargement of the prostate. Prostate volume (PV) measurement in large population-based studies would allow deriving more objective reference values and a more valid early BPH diagnosis. A fully automated method is therefore required. In the clinical context, the measurement of the PV is important for treatment response monitoring in the clinical applications for BPH management research, and an accurate method for PV is essential. Magnetic Resonance Imaging was used for PV estimation. Two methods based on the Support Vector Machines (SVM) were developed: the binary Support Vector Machines (C SVM)-based method for epidemiological studies and the single-class Support Vector Machines (S SVM)-based method for clinical studies. The second method was additionally compared to the ellipsoid formula for PV estimation, which is widespread in the clinic. The comparison between volume measurement of the C SVM-based method and manual delineation of observers A and B yielded a strong correlation (Spearmans rank correlation coefficients ñ of 0.936 [p < 0.001] and 0.859 [p < 0.001], respectively). Comparing the C SVM-based method and the two manual delineations by observers A and B shows an agreement with a mean difference of 3.0 ml (95% confidence interval of -3.1 to +9.2 ml) and 1.9 ml (95% confidence interval of −7.1 to +10.8 ml), respectively. The S SVM-based method and the reference PV (manual delineation of observer A) show excellent correlation (Spearmans rank correlation coefficient ñ = 0.965, p < 0.001), while the ellipsoid formula is less well correlated with the reference PV (Spearmans rank correlation coefficient ñ = 0.873, p < 0.001). The mean difference between S SVM and the reference PV was −0.05 ml (95% confidence interval of −3.8 to +3.7 ml); on the other hand, the mean difference between the ellipsoid formula and the reference PV was much greater, with 8.6 ml (95% confidence interval of +1 to +16.2 ml). The C SVM-based method has considerable potential for integration in epidemiological studies. The prostate volumes obtained by the S SVM-based method agreed excellently with the reference and would be clinically useful for urologists in prostate volumetric analysis.