Segmentation of Overlapping Cervical Smear Cells Based on U-Net and Improved Level Set

Abstract

Full convolution network (FCN) is widely used in medical image segmentation and its performance is better than other conventional techniques. This paper proposes a new fusion algorithm that combined the convolutional neural network U-net with a new modified level set method to segment overlapping cervical smear cells. U-net could provide more excellent segmentation results of nuclei and cytoplasm cluster. Then, a modified level set energy function with distance map and a new shape prior term is applied to extract the contour of cervical cells. Owing to this new level set energy function, the segmentation of every individual cell performed well, especially in overlapping area of cells. The evaluation of results also proves the improvement of our fusion algorithm.