Improved Robustness for Deep Learning-based Segmentation of Perfusion CMR Using Data Adaptive Uncertainty-guided Spatiotemporal Analysis

We proposed and validated Data Adaptive Uncertainty-Guided Spatiotemporal (DAUGS) analysis that leverages the data-driven uncertainty map of the segmentation contours among a pool of trained deep neural networks (DNNs) and automatically selects the segmentation result with the highest level of certainty. Our results suggest that proposed DAUGS and standard DNN-based analysis demonstrated on-par performance on the internal test set which is from the same institution as training set and acquired with FLASH sequence. In contrast, DAUGS analysis considerably outperformed DNN-based analysis on the external test set which was acquired with a bSSFP pulse sequence at a different institution, demonstrating the improved robustness of the proposed method despite limited training data.