Automated Semantic Segmentation for Volumetric Cardiovascular Feature Quantification and Pathology AssessmentWe present a pipeline method that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification as a fully automatic processing pipeline. The initial element consists of a 2D U-Net convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The results of the segmentation were used to compute a comprehensive volumetric feature matrix that captured diagnostic clinical procedure data and that was used to model a cardiac pathology classifier.Our approach evaluated anonymized parasternal MRI cardiac images from a database of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. We achieved top average Dice index scores of 0.939, 0.849, 0.886 for structure segmentation of the left ventricle (LV), right ventricle (RV) and myocardium respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using our trained model.
Document ID
20190033071
Acquisition Source
Ames Research Center
Document Type
Abstract
Authors
Lindsey, Tony (NASA Ames Research Center Moffett Field, CA, United States)
Lee, Rebecca Sawyer (Stinger Ghaffarian Technologies Inc. (SGT Inc.) Moffett Field, CA, United States)
Date Acquired
November 18, 2019
Publication Date
September 9, 2018
Subject Category
Cybernetics, Artificial Intelligence And RoboticsAerospace Medicine
Report/Patent Number
ARC-E-DAA-TN59550
Meeting Information
Meeting: 2018 Conference on Machine Intelligence in Medical Imaging