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 requir...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.展开更多
Most implantation cases are implemented using implants selected from the available standard set, but in some cases, only those implants conforming to individual patient's skeletal morphology can serve the purpose....Most implantation cases are implemented using implants selected from the available standard set, but in some cases, only those implants conforming to individual patient's skeletal morphology can serve the purpose. This paper proposes a new approach to design and fabricate custom-made exact-fit medical implants. With a real surgical case as the example,technical design details are presented; and three algorithms are given respectively for segmentation based on object features, triangular mesh defragmentation and mesh cutting.展开更多
文摘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.
文摘Most implantation cases are implemented using implants selected from the available standard set, but in some cases, only those implants conforming to individual patient's skeletal morphology can serve the purpose. This paper proposes a new approach to design and fabricate custom-made exact-fit medical implants. With a real surgical case as the example,technical design details are presented; and three algorithms are given respectively for segmentation based on object features, triangular mesh defragmentation and mesh cutting.
