1临床资料患者男,76岁,因“发作性胸痛半年余,加重1周”入院。查体:脉搏74次/min,血压138/73 mm Hg(1 mm Hg=0.133 k Pa),口唇无发绀,肝颈静脉反流征阴性,双肺呼吸音清。心界正常,心率74次/min,律齐,无杂音。心电图检查示V1-5导联ST-T...1临床资料患者男,76岁,因“发作性胸痛半年余,加重1周”入院。查体:脉搏74次/min,血压138/73 mm Hg(1 mm Hg=0.133 k Pa),口唇无发绀,肝颈静脉反流征阴性,双肺呼吸音清。心界正常,心率74次/min,律齐,无杂音。心电图检查示V1-5导联ST-T变化。心肌肌钙蛋白(cardiac troponin,cTn)I 2.46μg/L,诊断为急性非ST段抬高型心肌梗死。冠状动脉造影显示罪犯病变前降支近中段弥漫性重度钙化狭窄,最重处95%狭窄(图1(1)),心肌梗死溶栓治疗(TIMI)血流分级3级。展开更多
Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the detai...Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the details of coronary calcification in vivo.In routine clinical practice,it is a time-consuming and laborious task for clinicians to review the over 250 images in a single pullback.Besides,the imbalance label distribution within the entire pullbacks is another problem,which could lead to the failure of the classifier model.Given the success of deep learning methods with other imaging modalities,a thorough understanding of calcified plaque detection using Convolutional Neural Networks(CNNs)within pullbacks for future clinical decision was required.Methods All 33 IVOCT clinical pullbacks of 33 patients were taken from Affiliated Drum Tower Hospital,Nanjing University between December 2017 and December 2018.For ground-truth annotation,three trained experts determined the type of plaque that was present in a B-Scan.The experts assigned the labels'no calcified plaque','calcified plaque'for each OCT image.All experts were provided the all images for labeling.The final label was determined based on consensus between the experts,different opinions on the plaque type were resolved by asking the experts for a repetition of their evaluation.Before the implement of algorithm,all OCT images was resized to a resolution of 300×300,which matched the range used with standard architectures in the natural image domain.In the study,we randomly selected 26 pullbacks for training,the remaining data were testing.While,imbalance label distribution within entire pullbacks was great challenge for various CNNs architecture.In order to resolve the problem,we designed the following experiment.First,we fine-tuned twenty different CNNs architecture,including customize CNN architectures and pretrained CNN architectures.Considering the nature of OCT images,customize CNN architectures were designed that the layers were fewer than 25 layers展开更多
文摘1临床资料患者男,76岁,因“发作性胸痛半年余,加重1周”入院。查体:脉搏74次/min,血压138/73 mm Hg(1 mm Hg=0.133 k Pa),口唇无发绀,肝颈静脉反流征阴性,双肺呼吸音清。心界正常,心率74次/min,律齐,无杂音。心电图检查示V1-5导联ST-T变化。心肌肌钙蛋白(cardiac troponin,cTn)I 2.46μg/L,诊断为急性非ST段抬高型心肌梗死。冠状动脉造影显示罪犯病变前降支近中段弥漫性重度钙化狭窄,最重处95%狭窄(图1(1)),心肌梗死溶栓治疗(TIMI)血流分级3级。
基金supported in part by the National Natural Science Foundation of China ( NSFC ) ( 11772093)ARC ( FT140101152)
文摘Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the details of coronary calcification in vivo.In routine clinical practice,it is a time-consuming and laborious task for clinicians to review the over 250 images in a single pullback.Besides,the imbalance label distribution within the entire pullbacks is another problem,which could lead to the failure of the classifier model.Given the success of deep learning methods with other imaging modalities,a thorough understanding of calcified plaque detection using Convolutional Neural Networks(CNNs)within pullbacks for future clinical decision was required.Methods All 33 IVOCT clinical pullbacks of 33 patients were taken from Affiliated Drum Tower Hospital,Nanjing University between December 2017 and December 2018.For ground-truth annotation,three trained experts determined the type of plaque that was present in a B-Scan.The experts assigned the labels'no calcified plaque','calcified plaque'for each OCT image.All experts were provided the all images for labeling.The final label was determined based on consensus between the experts,different opinions on the plaque type were resolved by asking the experts for a repetition of their evaluation.Before the implement of algorithm,all OCT images was resized to a resolution of 300×300,which matched the range used with standard architectures in the natural image domain.In the study,we randomly selected 26 pullbacks for training,the remaining data were testing.While,imbalance label distribution within entire pullbacks was great challenge for various CNNs architecture.In order to resolve the problem,we designed the following experiment.First,we fine-tuned twenty different CNNs architecture,including customize CNN architectures and pretrained CNN architectures.Considering the nature of OCT images,customize CNN architectures were designed that the layers were fewer than 25 layers
