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Prostate segmentation from 3D transrectal ultrasound using statistical shape models and various appearance models

Due to the high noise and artifacts typically encountered in ultrasound images, segmenting objects from this modality is one of the most challenging tasks in medical image analysis. Model-based approaches like statistical shape models (SSMs) incorporate prior knowledge that supports object detection in case of incomplete evidence from the image data. How well the model adapts to an unseen image is primarily determined by the suitability of the used appearance model, which evaluates the goodness of fit during model evolution. In this paper, we compare two gradient profile models with a region-based approach featuring local histograms to detect the prostate in 3D transrectal ultrasound (TRUS) images. All models are used within an SSM segmentation framework with optimal surface detection for outlier removal. Evaluation was performed using cross-validation on 35 datasets. While the histogram model failed in 10 cases, both gradient models had only 2 failures and reached an average surface distance of 1.16 ± 0.38 mm in comparison with interactively generated reference contours.