A multi-scale recurrent fully convolution neural network for laryngeal leukoplakia segmentation

Abstract

Laryngeal leukoplakia is one kind of precancerous lesions in the larynx. Precise detection and segmentation of leukoplakia in laryngoscopic images is important for laryngeal disease diagnosis and treatment. In this paper, we proposed a multi-scale recurrent fully convolution neural network named boldface-M-Net (BM-Net) to identify and segment laryngeal leukoplakia lesions. The proposed BM-Net was composed of a multi-scale input layer, a double U-shaped convolution network, and a side-output layer. First, we augmented the image to produce six channels and then constructed image pyramids for the multi-scale input layer. For the U-shaped convolution network, we constructed a new U-Net using multi-scale convolution and a recurrent convolution layer (RCL) instead of the original convolution layer. We then employed skip connections to connect the double U-shaped convolution network, one with three 2 × 2 max pooling layers and the other with four, thus forming the main structure of BM-Net. We added the output for the three-layered U-Net to the side-output layer to produce a companion local prediction map for each scale layer. Image pyramids and multi-scale convolution can generate multiple level-receptive fields, while the RCL allows for the greater perception of context with parameter t increases. Finally, we compared the performance of the proposed BM-Net with the popular networks, including FCN-8s, Seg-Net, U-Net, M-Net, and three other modified networks for segmenting laryngeal leukoplakia in laryngoscopic images. According to the experimental results, BM-Net, which inherited the advantages of U-Net, M-Net, and RCL, exhibited overall better performance in laryngeal leukoplakia segmentation than the other networks.