12 March 2018 Ventricular segmentation and quantitative assessment in cardiac MR using convolutional neural networks
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Abstract
Segmentation of heart substructures in cardiac magnetic resonance (CMR) is an important step in the quantitative assessment of the impact of cardiovascular disease. Manual delineation of these structures, over many patients and multiple time phases, is time consuming and prone to human error and fatigue. In this work we use a deep fully convolutional neural network architecture to automatically segment heart substructures in CMR, achieving state of the art results on a recent benchmark dataset. We further apply our process to a much larger study of CMR subjects, automatically segmenting both left and right ventricular endocardiums (LV, RV) with full thirty-phase time resolution, and LV epicardium (Epi) at end-diastole. We validate our automatically obtained results against manual delineations using Dice overlap and Hausdorff distance, as well as Bland-Altman limits of agreement on the derived blood volumes, ejection fraction, and LV mass. We obtain median Dice overlaps of 0.97, 0.94, and 0.97 on the three structures respectively, and further find small biases and narrow limits of agreement between the two assessments (manual, automatic) of volumes and mass. Our results show promise for the fully automated analysis of the CMR data stream in the near future.
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Joshua V. Stough, Joshua V. Stough, Joseph DiPalma, Joseph DiPalma, Zilin Ma, Zilin Ma, Brandon K. Fornwalt, Brandon K. Fornwalt, Christopher M. Haggerty, Christopher M. Haggerty, } "Ventricular segmentation and quantitative assessment in cardiac MR using convolutional neural networks", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1057826 (12 March 2018); doi: 10.1117/12.2291534; https://doi.org/10.1117/12.2291534
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