Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both extern...Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.展开更多
针对CREO三维线缆具有柔性可随意变形特性而引起难以进行干涉检测的问题,提出基于离散点距离的三维线缆干涉检测算法。该算法通过线缆中心线动态离散点对三维线缆进行表达,分析结构件的空间位置关系,利用线缆与线缆、线缆与结构件之间...针对CREO三维线缆具有柔性可随意变形特性而引起难以进行干涉检测的问题,提出基于离散点距离的三维线缆干涉检测算法。该算法通过线缆中心线动态离散点对三维线缆进行表达,分析结构件的空间位置关系,利用线缆与线缆、线缆与结构件之间的空间位置关系进行干涉检测,实现了对三维线缆进行快速干涉检测的目的。最后在Visual Studio 2010与CREO平台上采用C++语言和TOOLKIT工具对所提算法进行了验证。结果表明该算法具有较好的快速性与准确性,可以满足工程实际需求。展开更多
文摘Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.
文摘针对CREO三维线缆具有柔性可随意变形特性而引起难以进行干涉检测的问题,提出基于离散点距离的三维线缆干涉检测算法。该算法通过线缆中心线动态离散点对三维线缆进行表达,分析结构件的空间位置关系,利用线缆与线缆、线缆与结构件之间的空间位置关系进行干涉检测,实现了对三维线缆进行快速干涉检测的目的。最后在Visual Studio 2010与CREO平台上采用C++语言和TOOLKIT工具对所提算法进行了验证。结果表明该算法具有较好的快速性与准确性,可以满足工程实际需求。
