A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series.

A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis.