Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic
  Stroke on Non-contrast CT
Sophie Ostmeier Brian Axelrod Benjamin Pulli Benjamin F. J. Verhaaren Abdelkader Mahammedi Yongkai Liu Christian Federau Greg Zaharchuk Jeremy J. Heit
Purpose: Multi-expert deep learning training methods to automatically
quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The
data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic
stroke patients recruited in the DEFUSE 3 trial. A benchmark U-Net was trained
on the reference annotations of three experienced neuroradiologists to segment
ischemic brain tissue using majority vote and random expert sampling training
schemes. We used a one-sided Wilcoxon signed-rank test on a set of segmentation
metrics to compare bootstrapped point estimates of the training schemes with
the inter-expert agreement and ratio of variance for consistency analysis. We
further compare volumes with the 24h-follow-up DWI (final infarct core) in the
patient subgroup with full reperfusion and we test volumes for correlation to
the clinical outcome (mRS after 30 and 90 days) with the Spearman method.
Results: Random expert sampling leads to a model that shows better agreement
with experts than experts agree among themselves and better agreement than the
agreement between experts and a majority-vote model performance (Surface Dice
at Tolerance 5mm improvement of 61% to 0.70 +- 0.03 and Dice improvement of 25%
to 0.50 +- 0.04). The model-based predicted volume similarly estimated the
final infarct volume and correlated better to the clinical outcome than CT
perfusion. Conclusion: A model trained on random expert sampling can identify
the presence and location of acute ischemic brain tissue on Non-Contrast CT
similar to CT perfusion and with better consistency than experts. This may
further secure the selection of patients eligible for endovascular treatment in
less specialized hospitals.