For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employi...For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employing fuzzy logic priority is proposed for opportunistic array radar（OAR）. In this scheme,the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section（RCS）, the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Crame ′r-Rao lower bound（BCRLB） provides us with a low bound of localization estimation root-mean-square error（RMSE）, and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm（GA） to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.展开更多
D-SOAR PLUS G 40000 W特高功率高速光纤激光切割机,主要针对高端客户精心配置,整机由瑞士百超集团技术团队领衔设计,主要零部件采用原装进口品牌,具有切割精度高、速度更快、操作更简单、使用成本更低和切割材料更广泛等特点,为客户提...D-SOAR PLUS G 40000 W特高功率高速光纤激光切割机,主要针对高端客户精心配置,整机由瑞士百超集团技术团队领衔设计,主要零部件采用原装进口品牌,具有切割精度高、速度更快、操作更简单、使用成本更低和切割材料更广泛等特点,为客户提供了稳定的激光切割性能,减少了板材浪费,提高了生产能力。展开更多
Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substan...Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substantial user variation.To enhance the efficiency and consistency,we developed a unified deep learning(DL)OAR contouring model,DeepOAR,that was trained using multiple partially labeled datasets for segmenting a comprehensive set of thoracic OARs following the Radiation Therapy Oncology Group(RTOG)-guided OAR atlas.This DL model supports the segmentation of six required and eight optional OARs guided by the NRG-RTOG 1106 trial,providing precise and reproducible OARs contouring that are ready to be used in radiotherapy practice.Materials and methods:Following the OAR contouring recommendation of the NRG-RTOG 1106 trial,we collected and curated three private datasets and two public datasets,comprising a total of 531 patients with partially annotated thoracic OARs.These partially annotated datasets were utilized to develop DeepOAR,which consisted of a shared encoder and 14 separate decoders,with each decoder dedicated to one specific OAR.For model training,we utilized all patients from the two public datasets and 75%of the patients from the private datasets.We reserved the remaining 25%of the private datasets for independent testing.A multi-user study involving 21 radiation oncologists was conducted on 40 randomly selected patients from the independent testing dataset to evaluate the clinical applicability of DeepOAR.The Dice coefficient score(DSC)and average surface distance(ASD)were computed to evaluate the quantitative delineation performance of the model.Results:DeepOAR outperformed nnUNet(the benchmark medical segmentation model)across all 14 OARs,achieving mean DSC and ASD values of 88.4%and 1.0 mm,respectively,in the independent testing set.Multi-user validation demonstrated that 89.7%of DeepOAR-generated OARs were clinically acceptable or required only minor revisions.A comparison using two randomly selected patients showed that the delineation variability of DeepOAR was significantly smaller than the inter-user variation among radiation oncologists.Human editing of DeepOAR’s predictions could further improve OAR delineation accuracy by an average of 3%increase in DSC and 40%reduction in ASD while significantly reducing the workload of radiation oncologists for contouring 14 thoracic OARs by an average of 77.0%.Conclusion:We developed DeepOAR,a DL-based unified contouring model trained using multiple partially labeled datasets,to delineate a comprehensive set of 14 thoracic OARs following the RTOG-guided OAR atlas.Both qualitative and quantitative results demonstrated the strong clinical applicability of DeepOAR for the OAR delineation process in thoracic cancer radiotherapy workflows,along with improved efficiency,comprehensiveness,and quality.展开更多
基金supported by the National Natural Science Foundation of China(6127132761671241)
文摘For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employing fuzzy logic priority is proposed for opportunistic array radar（OAR）. In this scheme,the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section（RCS）, the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Crame ′r-Rao lower bound（BCRLB） provides us with a low bound of localization estimation root-mean-square error（RMSE）, and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm（GA） to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.
文摘D-SOAR PLUS G 40000 W特高功率高速光纤激光切割机,主要针对高端客户精心配置,整机由瑞士百超集团技术团队领衔设计,主要零部件采用原装进口品牌,具有切割精度高、速度更快、操作更简单、使用成本更低和切割材料更广泛等特点,为客户提供了稳定的激光切割性能,减少了板材浪费,提高了生产能力。
基金Xianghua Ye,Zhejiang Provincial Science and Technology Project,2024-KY1-001-105.This funding is intended to support the training of organ auto-segmentation models.
文摘Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substantial user variation.To enhance the efficiency and consistency,we developed a unified deep learning(DL)OAR contouring model,DeepOAR,that was trained using multiple partially labeled datasets for segmenting a comprehensive set of thoracic OARs following the Radiation Therapy Oncology Group(RTOG)-guided OAR atlas.This DL model supports the segmentation of six required and eight optional OARs guided by the NRG-RTOG 1106 trial,providing precise and reproducible OARs contouring that are ready to be used in radiotherapy practice.Materials and methods:Following the OAR contouring recommendation of the NRG-RTOG 1106 trial,we collected and curated three private datasets and two public datasets,comprising a total of 531 patients with partially annotated thoracic OARs.These partially annotated datasets were utilized to develop DeepOAR,which consisted of a shared encoder and 14 separate decoders,with each decoder dedicated to one specific OAR.For model training,we utilized all patients from the two public datasets and 75%of the patients from the private datasets.We reserved the remaining 25%of the private datasets for independent testing.A multi-user study involving 21 radiation oncologists was conducted on 40 randomly selected patients from the independent testing dataset to evaluate the clinical applicability of DeepOAR.The Dice coefficient score(DSC)and average surface distance(ASD)were computed to evaluate the quantitative delineation performance of the model.Results:DeepOAR outperformed nnUNet(the benchmark medical segmentation model)across all 14 OARs,achieving mean DSC and ASD values of 88.4%and 1.0 mm,respectively,in the independent testing set.Multi-user validation demonstrated that 89.7%of DeepOAR-generated OARs were clinically acceptable or required only minor revisions.A comparison using two randomly selected patients showed that the delineation variability of DeepOAR was significantly smaller than the inter-user variation among radiation oncologists.Human editing of DeepOAR’s predictions could further improve OAR delineation accuracy by an average of 3%increase in DSC and 40%reduction in ASD while significantly reducing the workload of radiation oncologists for contouring 14 thoracic OARs by an average of 77.0%.Conclusion:We developed DeepOAR,a DL-based unified contouring model trained using multiple partially labeled datasets,to delineate a comprehensive set of 14 thoracic OARs following the RTOG-guided OAR atlas.Both qualitative and quantitative results demonstrated the strong clinical applicability of DeepOAR for the OAR delineation process in thoracic cancer radiotherapy workflows,along with improved efficiency,comprehensiveness,and quality.
