Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN

PURPOSE: To apply deep convolutional neural networks (CNN) to the left ventricular segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging. To develop methods that measure uncertainty and that adapt segmentation based on a specific false positive vs. false negative tradeoff. METHODS: We utilized a modified UNET architecture with Monte Carlo (MC) dropout. The model was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and quantitative myocardial blood flow (MBF) maps were available for comparison. We consider two global scores of segmentation uncertainty, "Dice Uncertainty" and "MC Uncertainty", which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter $\beta$ was used to adapt the model to a specific false positive vs. false negative tradeoff. RESULTS: The modified UNET model achieved Dice coefficient of mean(std) = $0.91(0.04)$ on the test set. MBF measured using automatic segmentation was highly correlated to that measured using the manual segmentation ($R^2 = 0.96$). Dice Uncertainty and MC Uncertainty were in good agreement ($R^2 = 0.64$). As $\beta$ increased, the false positive rate systematically decreased and false negative rate systematically increased. CONCLUSION: We demonstrate the feasibility of using CNN for automatic segmentation of the left ventricle in myocardial ASL data. This is a particularly challenging application because of low and inconsistent blood-myocardium contrast-to-noise ratio. We also demonstrate novel methods that measure uncertainty and adapt to a desired tradeoff between false positive and false negative rates.