  Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is
a widely used modality for diagnosing myocardial blood flow (perfusion)
abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300
time-resolved images of myocardial perfusion are acquired at various contrast
"wash in/out" phases. Manual segmentation of myocardial contours in each
time-frame of a DCE image series can be tedious and time-consuming,
particularly when non-rigid motion correction has failed or is unavailable.
While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI
datasets, a "dynamic quality control" (dQC) technique for reliably detecting
failed segmentations is lacking. Here we propose a new space-time uncertainty
metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI
datasets by validating the proposed metric on an external dataset and
establishing a human-in-the-loop framework to improve the segmentation results.
In the proposed approach, we referred the top 10% most uncertain segmentations
as detected by our dQC tool to the human expert for refinement. This approach
resulted in a significant increase in the Dice score (p<0.001) and a notable
decrease in the number of images with failed segmentation (16.2% to 11.3%)
whereas the alternative approach of randomly selecting the same number of
segmentations for human referral did not achieve any significant improvement.
Our results suggest that the proposed dQC framework has the potential to
accurately identify poor-quality segmentations and may enable efficient
DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical
interpretation and reporting of dynamic CMRI datasets.
