Cardiac MRI Left Ventricle Segmentation and Quantification: A Framework Combining U-Net and Continuous Max-Flow - Guo, Fumin; Ng, Matthew; Wright, Graham (Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, 2019)
Cardiac magnetic resonance imaging (MRI) is routinely used for cardiovascular disease diagnosis and therapy guidance. Left ventricle (LV) segmentation is typically required as a first step to quantify cardiac indices. In this work, we developed an automatic approach for LV segmentation and indices quantification of cardiac MRI. We employed a U-net convolutional neural network to generate LV segmentation probability maps. The initial probability maps were used to provide the labeling cost measurements of a continuous min-cut segmentation model and the final segmentation was regularized using image edge information. The continuous min-cut segmentation model was solved globally and exactly through convex relaxation and dual optimization on a GPU. We applied our approach to a clinical dataset of 45 subjects and achieved a mean DSC of $$89.4± 5.0%$$ and average symmetric surface distance of $$0.81± 0.31$$ mm for LV myocardium segmentation. For LV indices quantification, we observed a mean absolute error of 114.8 mm $$^2$$ for LV cavity, 168.6 mm $$^2$$ for LV myocardium, $$\sim $$ 1.8 mm for LV cavity dimensions, and 1.2 $${\sim }$$ 1.6 mm for LV myocardium wall thickness measurements. These results suggest that our framework provide the potential for LV function quantification using cardiac MRI.