5 research outputs found
Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT
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
Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists
Purpose: To develop a deep learning model to segment the acute ischemic
infarct on non-contrast Computed Tomography (NCCT). Materials and Methods In
this retrospective study, 227 Head NCCT examinations from 200 patients enrolled
in the multicenter DEFUSE 3 trial were included. Three experienced
neuroradiologists (experts A, B and C) independently segmented the acute
infarct on each study. The dataset was randomly split into 5 folds with
training and validation cases. A 3D deep Convolutional Neural Network (CNN)
architecture was optimized for the data set properties and task needs. The
input to the model was the NCCT and the output was a segmentation mask. The
model was trained and optimized on expert A. The outcome was assessed by a set
of volume, overlap and distance metrics. The predicted segmentations of the
best model and expert A were compared to experts B and C. Then we used a paired
Wilcoxon signed-rank test in a one-sided test procedure for all metrics to test
for non-inferiority in terms of bias and precision. Results: The best
performing model reached a Surface Dice at Tolerance (SDT)5mm of 0.68 \pm 0.04.
The predictions were non-inferior when compared to independent experts in terms
of bias and precision (paired one-sided test procedure for differences in
medians and bootstrapped standard deviations with non-inferior boundaries of
-0.05, 2ml, and 2mm, p < 0.05, n=200). Conclusion: For the segmentation of
acute ischemic stroke on NCCT, our 3D CNN trained with the annotations of one
neuroradiologist is non-inferior when compared to two independent
neuroradiologists
Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists
Abstract We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists’ (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3 ml, and 3 mm, respectively. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46 ± 0.09 Surface Dice at Tolerance 5mm and 0.47 ± 0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, p < 0.05 . The before, CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists
sj-docx-1-ine-10.1177_15910199231170411 - Supplemental material for Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach*
Supplemental material, sj-docx-1-ine-10.1177_15910199231170411 for Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach* by Reza M Taghavi, Guangming Zhu, Max Wintermark, Gabriella M Kuraitis, Eric S Sussman, Benjamin Pulli, Brook Biniam, Sophie Ostmeier and
Gary K Steinberg, Jeremy J Heit in Interventional Neuroradiology</p