Prostate segmentation from 2D ultrasound images

Abstract

Outlining, or segmenting, the prostate is a very important task in the assignment of appropriate therapy and dose for cancer treatment; however, manual outlining is a tedious and time-consuming task. In this paper, an algorithm is described for semi-automatic segmentation of the prostate from 2D ultrasound images. The algorithm uses model-based initialization and the efficient discrete dynamic contour. Initialization requires the user to select only four points from which the outline of the prostate is estimated using cubic interpolation functions and shape information. The estimated contour is then deformed automatically to better fit the image. The algorithm can easily segment a wide range of prostate images, and contour editing tools are included to handle more difficult cases. The performance of the algorithm with a single user was compared to manual outlining by a single expert observer. The average distance between semi-automatically and manually outlined boundaries was found to be less than 5 pixels (0.63 mm), and the accuracy and sensitivity were both over 90%.