Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL
  Segmentation using Deep CNN   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.

24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th