The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are ...The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.展开更多
The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment p...The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.展开更多
In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedfr...In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.展开更多
双电源输入级联型电力电子变压器(dual power supply cascaded-type power electronic transformer,DPSC-PET)与两路供电电源相连,运行可靠性高、方式灵活,在中低压配电网中应用前景广泛,深入研究其电压暂降耐受能力与调节方法,对于维...双电源输入级联型电力电子变压器(dual power supply cascaded-type power electronic transformer,DPSC-PET)与两路供电电源相连,运行可靠性高、方式灵活,在中低压配电网中应用前景广泛,深入研究其电压暂降耐受能力与调节方法,对于维持暂降期间DPSC-PET的高效能量传输、保证系统优质供电具有重要意义。首先,分析DPSC-PET的拓扑与控制策略;其次,针对引起系统传输功率缺额最严重的三相对称电压暂降,分析DPSC-PET暂降耐受能力限制因素;然后,从功率平衡角度出发,提出一种不同输入侧发生暂降时DPSC-PET耐受能力实时分析与双输入端口功率协同调节方法,以实现暂降下低压直流母线电压恢复,提升DPSC-PET应对暂态扰动的能力;最后,搭建DPSC-PET仿真模型,对不同输入侧发生不同程度的电压暂降场景进行仿真。结果表明,所提调节方法能有效提升DPSC-PET的暂降耐受能力。展开更多
The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/D...The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.展开更多
At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important i...At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.展开更多
Backgrounds and Purpose—In indolent non-Hodgkin lymphoma, histological transformation is a dramatic event which reduces the prognosis significantly. SUVmax values from FDG-PET/CT help differentiate between aggressive...Backgrounds and Purpose—In indolent non-Hodgkin lymphoma, histological transformation is a dramatic event which reduces the prognosis significantly. SUVmax values from FDG-PET/CT help differentiate between aggressive and indolent lymphomas, and transformed indolent lymphomas also show an increased FDG uptake. Possibly FDG uptake increases early in the clinical course and could predict histological transformation. Our objective was to predict histological transformation in indolent lymphomas from initial staging FDG-PET/CT. Patients and Methods—A retrospective study was performed. Patients with biopsy-proven indolent lymphoma who had had initial staging FDG-PET/CT were included. Qualitative (foci compared with FDG uptake liver) and semiquantitative (SUVmax-value per focus) analyses were performed of all abnormal foci. Patient characteristics and outcome were evaluated. Results—We included 88 patients, 5 of whom developed a histological transformation. Semiquantitative analysis showed a relation between maximum standardized uptake value and histological transformation (odds ratio 1.25, 95% CI 1.024 - 1.513). Qualitative analysis showed a negative predictive relation of FDG uptake less than or equal to liver in the occurrence of histological transformation. Transformation-free survival was 100% over 30 months in those with FDG uptake lower than or equal to liver. More FDG uptake than liver showed transformation-free survival of 88% over 30 months. Conclusion—Qualitative analysis of staging FDG-PET/CT in indolent lymphomas could be useful to rule out transformation in the next 30 months. In our study, semiquantitative analysis was statistically significantly associated with histological transformation and maximum standardized uptake value. However, because of the small number of patients, cautious interpretation of the results is warranted. More studies are needed to investigate the role of staging PET/CT in patient with indolent non-Hodkin lymphoma in the prediction of transformation.展开更多
基金funded by National Natural Science Foundation of China No.62062003Ningxia Natural Science Foundation Project No.2023AAC03293.
文摘The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.
基金supported by Scientific Research Deanship at University of Ha’il,Saudi Arabia through project number RG-23137.
文摘The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.
基金the National Natural Science Foundation of China(No.82160347)Yunnan Provincial Science and Technology Department(No.202102AE090031)Yunnan Key Laboratory of Smart City in Cyberspace Security(No.202105AG070010).
文摘In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.
文摘双电源输入级联型电力电子变压器(dual power supply cascaded-type power electronic transformer,DPSC-PET)与两路供电电源相连,运行可靠性高、方式灵活,在中低压配电网中应用前景广泛,深入研究其电压暂降耐受能力与调节方法,对于维持暂降期间DPSC-PET的高效能量传输、保证系统优质供电具有重要意义。首先,分析DPSC-PET的拓扑与控制策略;其次,针对引起系统传输功率缺额最严重的三相对称电压暂降,分析DPSC-PET暂降耐受能力限制因素;然后,从功率平衡角度出发,提出一种不同输入侧发生暂降时DPSC-PET耐受能力实时分析与双输入端口功率协同调节方法,以实现暂降下低压直流母线电压恢复,提升DPSC-PET应对暂态扰动的能力;最后,搭建DPSC-PET仿真模型,对不同输入侧发生不同程度的电压暂降场景进行仿真。结果表明,所提调节方法能有效提升DPSC-PET的暂降耐受能力。
基金supported by National Key Research and Development Program of China (2016YFB0900500,2017YFB0903100)the State Grid Science and Technology Project (SGRI-DL-F1-51-011)
文摘The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.
基金the National Key Research and Development Program of China(2017YFB0903200).
文摘At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.
文摘Backgrounds and Purpose—In indolent non-Hodgkin lymphoma, histological transformation is a dramatic event which reduces the prognosis significantly. SUVmax values from FDG-PET/CT help differentiate between aggressive and indolent lymphomas, and transformed indolent lymphomas also show an increased FDG uptake. Possibly FDG uptake increases early in the clinical course and could predict histological transformation. Our objective was to predict histological transformation in indolent lymphomas from initial staging FDG-PET/CT. Patients and Methods—A retrospective study was performed. Patients with biopsy-proven indolent lymphoma who had had initial staging FDG-PET/CT were included. Qualitative (foci compared with FDG uptake liver) and semiquantitative (SUVmax-value per focus) analyses were performed of all abnormal foci. Patient characteristics and outcome were evaluated. Results—We included 88 patients, 5 of whom developed a histological transformation. Semiquantitative analysis showed a relation between maximum standardized uptake value and histological transformation (odds ratio 1.25, 95% CI 1.024 - 1.513). Qualitative analysis showed a negative predictive relation of FDG uptake less than or equal to liver in the occurrence of histological transformation. Transformation-free survival was 100% over 30 months in those with FDG uptake lower than or equal to liver. More FDG uptake than liver showed transformation-free survival of 88% over 30 months. Conclusion—Qualitative analysis of staging FDG-PET/CT in indolent lymphomas could be useful to rule out transformation in the next 30 months. In our study, semiquantitative analysis was statistically significantly associated with histological transformation and maximum standardized uptake value. However, because of the small number of patients, cautious interpretation of the results is warranted. More studies are needed to investigate the role of staging PET/CT in patient with indolent non-Hodkin lymphoma in the prediction of transformation.
