Research on Lightweight Network Segmentation of Lung Parenchyma CT Image Based on FPGA Platform

Image segmentation algorithms for medical embedded vision devices usually require a light and low latency model. In this study, a novel lightweight DepthWise U-shape network (DWU-net) is proposed to address this issue, which implements the task of lung parenchyma image segmentation. In the contracting path and expanding path of segmentation network, we introduce a separable convolution unit to replace standard convolution for image feature extraction, which can learn the unique features of each layer of the image from multiple perspectives, and has more advantages in feature expression. Our algorithm has better flexibility, comparing to the original model, the model parameters' number has been greatly reduced and the time efficiency is fully improved. The proposed architecture achieves good performance in FPGA implementation.