Prostate ultrasound image segmentation using level set-based region flow with shape guidance

Prostate segmentation in ultrasound images is a clinically important and technically challenging task. Despite several research attempts, few effective methods are available. One problem is the limited algorithmic robustness to common artifacts in clinical data sets. To improve the robustness, we have developed a hybrid level set method, which incorporates shape constraints into a region-based curve evolution process. The online segmentation method alternates between two steps, namely, shape model estimation (ME) and curve evolution (CE). The prior shape information is encoded in an implicit parametric model derived offline from manually outlined training data. Utilizing this prior shape information, the ME step tries to compute the maximum a posteriori estimate of the model parameters. The estimated shape is then used to guide the CE step, which in turn provides a new model initialization for the ME step. The process stops automatically when the curve locks onto the specific prostate shape. The ME and the CE steps complement each other to capture both global and local shape details. With shape guidance, this algorithm is less sensitive to initial contour placement and more robust even in the presence of large boundary gaps and strong clutter. Promising results are demonstrated on both synthetic and real prostate ultrasound images.