Active Contours For Multi-Region Segmentation With A Convolutional Neural Network Initialization

Active contour-based methods are widely popular in the image segmentation field. Basically, they perform a semiautomatic region identification by partitioning the image content mainly into the foreground and background. Nevertheless, the accurate delimitation still remains as an important challenge which usually depends on how close the initial contour is placed to the object of interest (OI). Several applications of active contours require the user interaction to give prior information about the initial position as the first step, which drives the tool substantially dependent on a manual process. This paper describes how to overcome this limitation by including the expertise provided by the training stage of a Convolutional Neural Network (CNN). Despite CNN methods require a large dataset or data augmentation techniques to improve their results, the combined proposal accomplishes a pre-segmentation task with a reduced number of images to obtain the assumed locations for each OI. These results are used to initialize a multiphase active contour model that follows a level set scheme to lead a smoother multiregion segmentation with less effort. Experiments of this approach are included to compare classic techniques of contour initialization and show the benefits of our proposal.