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
Accurate and automated segmentation of 3D biomedical images is a sophisticated imperative in clinical diagnosis,imaging-guided surgery,and prognosis judgment.Although the burgeoning of deep learning technologies has f...Accurate and automated segmentation of 3D biomedical images is a sophisticated imperative in clinical diagnosis,imaging-guided surgery,and prognosis judgment.Although the burgeoning of deep learning technologies has fostered smart segmentators,the successive and simultaneous garnering global and local features still remains challenging,which is essential for an exact and efficient imageological assay.To this end,a segmentation solution dubbed the mixed parallel shunted transformer(MPSTrans)is developed here,highlighting 3DMPST blocks in a U-form framework.It enabled not only comprehensive characteristic capture and multiscale slice synchronization but also deep supervision in the decoder to facilitate the fetching of hierarchical representations.Performing on an unpublished colon cancer data set,this model achieved an impressive increase in dice similarity coefficient(DSC)and a 1.718 mm decease in Hausdorff distance at 95%(HD95),alongside a substantial shrink of computational load of 56.7%in giga floating-point operations per second(GFLOPs).Meanwhile,MPSTrans outperforms other mainstream methods(Swin UNETR,UNETR,nnU-Net,PHTrans,and 3D U-Net)on three public multiorgan(aorta,gallbladder,kidney,liver,pancreas,spleen,stomach,etc.)and multimodal(CT,PET-CT,and MRI)data sets of medical segmentation decathlon(MSD)brain tumor,multiatlas labeling beyond cranial vault(BCV),and automated cardiac diagnosis challenge(ACDC),accentuating its adaptability.These results reflect the potential of MPSTrans to advance the state-of-the-art in biomedical imaging analysis,which would offer a robust tool for enhanced diagnostic capacity.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.
基金supported by National Natural Science Foundation of China(Grant Nos.22204077,22374076)Natural Science Foundation of Jiangsu Province(BK20231455)+2 种基金Open Research Program of National Major Scientific and Technological Infrastructure for Translational Medicine(TMSK-2024-115)Fundamental Research Funds for the Central Universities(30922010501,2023303002)State Key Laboratory for Analytical Chemistry for Life Science(SKLACLS2402).
文摘Accurate and automated segmentation of 3D biomedical images is a sophisticated imperative in clinical diagnosis,imaging-guided surgery,and prognosis judgment.Although the burgeoning of deep learning technologies has fostered smart segmentators,the successive and simultaneous garnering global and local features still remains challenging,which is essential for an exact and efficient imageological assay.To this end,a segmentation solution dubbed the mixed parallel shunted transformer(MPSTrans)is developed here,highlighting 3DMPST blocks in a U-form framework.It enabled not only comprehensive characteristic capture and multiscale slice synchronization but also deep supervision in the decoder to facilitate the fetching of hierarchical representations.Performing on an unpublished colon cancer data set,this model achieved an impressive increase in dice similarity coefficient(DSC)and a 1.718 mm decease in Hausdorff distance at 95%(HD95),alongside a substantial shrink of computational load of 56.7%in giga floating-point operations per second(GFLOPs).Meanwhile,MPSTrans outperforms other mainstream methods(Swin UNETR,UNETR,nnU-Net,PHTrans,and 3D U-Net)on three public multiorgan(aorta,gallbladder,kidney,liver,pancreas,spleen,stomach,etc.)and multimodal(CT,PET-CT,and MRI)data sets of medical segmentation decathlon(MSD)brain tumor,multiatlas labeling beyond cranial vault(BCV),and automated cardiac diagnosis challenge(ACDC),accentuating its adaptability.These results reflect the potential of MPSTrans to advance the state-of-the-art in biomedical imaging analysis,which would offer a robust tool for enhanced diagnostic capacity.
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.
