Spatial-temporal segmentation of cine cardiac MRI time-series

Motivation: Cine cardiac MRI provides a way to quantify additional cardiac indices beyond ejection fraction, including ejection and filling rates, myocardial wall motion, and strain; segmentation of all temporal phases is required. Goal(s): To develop an approach to segmenting images in all cardiac phases in cine MRI. Approach: A U-net and a recurrent-neural-network were integrated to exploit the spatial-temporal information in cine time-series. 100 and 50 subjects labeled at the end-systole and end-diastole phases were used for network training and testing, respectively. Results: The use of spatial-temporal information substantially improved the segmentation accuracy and the algorithm cardiac indices were strongly correlated with manual measurements. Impact: The proposed method made effective use of the spatial-temporal information in a cine time-series and yielded highly accurate and precise segmentation and cardiac functional measurements, suggesting the utility of our approach for clinical cardiac patient care.