Jiyeong Kim, Kyung Hee Lim
Prognostic Factors for Hyperglycemia in Patients Receiving Chemotherapy
Cancer nursing
cardiac function quantification cardiovascular magnetic resonance requires precise contouring heart chambers timeconsuming task increasingly addressed plethora ever complex deep learning methods however small fraction made way academia clinical practice quality assessment control medical artificial intelligence opaque reasoning associated distinctive errors neural networks meet extraordinarily low tolerance failure aim study multilevel analysis comparison performance three popular convolutional neural network cnn models cardiac function quantification unet fcn multiresunet trained segmentation left right ventricles shortaxis cine images 119 patients clinical routine training pipeline hyperparameters kept constant isolate influence network architecture cnn performance evaluated expert segmentations 29 test cases contour level terms quantitative clinical parameters multilevel analysis included breakdown results slice position well visualization segmentation deviations linkage volume differences segmentation metrics via correlation plots qualitative analysis models showed strong correlation expert respect quantitative clinical parameters rz 0978 0977 0978 unet fcn multiresunet respectively multiresunet significantly underestimated ventricular volumes left ventricular myocardial mass segmentation difficulties failures clustered basal apical slices cnns largest volume differences basal slices mean absolute error per slice 42 45 ml basal 09 13 ml midventricular 09 09 ml apical slices results right ventricle higher variance outliers compared left ventricle intraclass correlation clinical parameters excellent 091 among cnns modifications cnn architecture critical quality error dataset despite good overall agreement expert errors accumulated basal apical slices models