  PURPOSE: To apply deep CNN to the segmentation task in myocardial arterial
spin labeled (ASL) perfusion imaging and to develop methods that measure
uncertainty and that adapt the CNN model to a specific false positive vs. false
negative tradeoff.
  METHODS: The Monte Carlo dropout (MCD) U-Net was trained on data from 22
subjects and tested on data from 6 heart transplant recipients. Manual
segmentation and regional myocardial blood flow (MBF) were available for
comparison. We consider two global uncertainty measures, named Dice Uncertainty
and MCD 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 MCD U-Net achieved Dice coefficient of mean(std) = 0.91(0.04) on
the test set. MBF measured using automatic segmentations was highly correlated
to that measured using the manual segmentation ($R^2$ = 0.96). Dice Uncertainty
and MCD 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 deep CNN for automatic
segmentation of myocardial ASL, with good accuracy. We also introduce two
simple methods for assessing model uncertainty. Finally, we demonstrate the
ability to adapt the CNN model to a specific false positive vs. false negative
tradeoff. These findings are directly relevant to automatic segmentation in
quantitative cardiac MRI and are broadly applicable to automatic segmentation
problems in diagnostic imaging.
