Keywords: Segmentation, Segmentation
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.
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