Deep Active Contour Network for Medical Image Segmentation

Abstract

Image segmentation is vital to medical image analysis and clinical diagnosis. Recently, convolutional neural networks (CNNs) have achieved tremendous success in this task, however, it performs poorly at recognizing precise object boundary due to the information loss in the successive downsampling layers. To overcome this problem, we integrate an active contour model (convexified Chan-Vese model) into the CNN structure (DenseUNet), forming a new framework called deep active contour network (DACN). Instead of manual setting, DACN applies a CNN backbone to learn the initialization and parameters of active contour model (ACM) automatically. The proposed DACN leverages the advantage of ACM to detect object boundaries accurately, which can be trained in an end-to-end differential manner. The experimental results on two public datasets demonstrate the effectiveness of DACN, and the trimap experiment confirms the superior ability of DACN to obtain precise boundary delineation.