摘要
Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.
作者
KANG Li
龚智鑫
黄建军
ZHOU Ziqi
KANG Li;GONG Zhixin;HUANG Jianjun;ZHOU Ziqi(College of Electronics and Information Engineering,Shenzhen University,Shenzhen 518000,China;Guangdong Key Laboratory of Intelligent Information Processing,Shenzhen 510006,China)
出处
《中国体视学与图像分析》
2023年第4期390-400,共11页
Chinese Journal of Stereology and Image Analysis
基金
国家自然科学基金(No.62171287)
深圳市科技项目(No.JCYJ20220818100004008)。
