Zonal segmentation of prostate using multispectral magnetic resonance images

Purpose: To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland. Methods: The proposed method is based on a modified version of the evidential C-means clustering algorithm. The evidential C-means optimization process was modified to introduce spatial neighborhood information. A priori knowledge of the prostate's zonal morphology was modeled as a geometric criterion and used as an additional data source to enhance the differentiation of the two zones. Results: Thirty-one clinical magnetic resonance imaging series were used to validate the method, and interobserver variability was taken into account in assessing its accuracy. The mean Dice Similarity Coefficient was 89 for the central gland and 80 for the peripheral zone, as validated by a consensus from expert radiologist segmentation. Conclusions: The method was statistically insensitive to variations in patient age, prostate volume and the presence of tumors, which increases its feasibility in a clinical context.