Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN.

ABSTRACT:

Purpose

To apply deep convolution neural network to the segmentation task in myocardial arterial spin labeled perfusion imaging and to develop methods that measure uncertainty and that adapt the convolution neural network model to a specific false-positive versus false-negative tradeoff.

Methods

The Monte Carlo dropout 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 were available for comparison. We consider 2 global uncertainty measures, named "Dice uncertainty" and "Monte Carlo dropout uncertainty," which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific

SUBMITTER: Do HP 

PROVIDER: S-EPMC6989045 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature