Interpretable Mathematical Model-guided Ultrasound Prostate Contour Extraction Using Data Mining Techniques

Among all image features, the contour is one of the most critical features for displaying the shape of the object intuitively. Due to unseen/missing regions of transrectal ultrasound images caused by imaging artifacts and limited field of view, accurate and robust ultrasound prostate contour extraction is challenging. Hence, we propose a triple cascaded framework for ultrasound prostate contour extraction using a few existing points as the prior. The proposed scheme contains two types of data mining: principal curve-based and machine learning-based methods. The first stage is using an improved polygonal segment method to obtain a contour composed of line segments connected by sorted vertices, where only a few radiologist-defined seed points are used as the prior. The second stage is to achieve an optimal machine learning-based approach based on an improved differential evolution-based method. The third stage is to find a map function (realized by the machine learning-based method) to generate the smooth contour represented by the output of neural network (i.e., optimized vertices) to match the ground truth contour. Our results demonstrated that the performance of the proposed method outperformed several other state-of-the-art methods.