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Visualizing disease-induced scarring and fibrosis in the heart on cardiacmagnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramountin characterizing disease progression and quantifying pathophysiologicalsubstrates of arrhythmias. However, segmentation and scar/fibrosisidentification from LGE-CMR is an intensive manual process prone to largeinter-observer variability. Here, we present a novel fully-automatedanatomically-informed deep learning solution for left ventricle (LV) andscar/fibrosis segmentation and clinical feature extraction from LGE-CMR. Thetechnology involves three cascading convolutional neural networks that segmentmyocardium and scar/fibrosis from raw LGE-CMR images and constrain thesesegmentations within anatomical guidelines, thus facilitating seamlessderivation of clinically-significant parameters. In addition to availableLGE-CMR images, training used "LGE-like" synthetically enhanced cine scans.Results show excellent agreement with those of trained experts in terms ofsegmentation (balanced accuracy of $96\%$ and $75\%$ for LV and scarsegmentation), clinical features ($2\%$ difference in mean scar-to-LV wallvolume fraction), and anatomical fidelity. Our segmentation technology isextendable to other computer vision medical applications and to problemsrequiring guidelines adherence of predicted outputs.

Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction