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

Association Between Intravenous Thrombolysis and Clinical Outcomes Among Patients With Ischemic Stroke and Unsuccessful Mechanical Reperfusion.

IMPORTANCE Clinical evidence of the potential treatment benefit of intravenous thrombolysis preceding unsuccessful mechanical thrombectomy (MT) is scarce. OBJECTIVE To determine whether intravenous thrombolysis (IVT) prior to unsuccessful MT improves functional outcomes in patients with acute ischemic stroke. DESIGN, SETTING, AND PARTICIPANTS Patients were enrolled in this retrospective cohort study from the prospective, observational, multicenter German Stroke Registry-Endovascular Treatment between May 1, 2015, and December 31, 2021. This study compared IVT plus MT vs MT alone in patients with acute ischemic stroke due to anterior circulation large-vessel occlusion in whom mechanical reperfusion was unsuccessful. Unsuccessful mechanical reperfusion was defined as failed (final modified Thrombolysis in Cerebral Infarction grade of 0 or 1) or partial (grade 2a). Patients meeting the inclusion criteria were matched by treatment group using 1:1 propensity score matching. INTERVENTIONS Mechanical thrombectomy with or without IVT. MAIN OUTCOMES AND MEASURES Primary outcome was functional independence at 90 days, defined as a modified Rankin Scale score of 0 to 2. Safety outcomes were the occurrence of symptomatic intracranial hemorrhage and death. RESULTS After matching, 746 patients were compared by treatment arms (median age, 78 [IQR, 68-84] years; 438 women [58.7%]). The proportion of patients who were functionally independent at 90 days was 68 of 373 (18.2%) in the IVT plus MT and 42 of 373 (11.3%) in the MT alone group (adjusted odds ratio [AOR], 2.63 [95% CI, 1.41-5.11]; P = .003). There was a shift toward better functional outcomes on the modified Rankin Scale favoring IVT plus MT (adjusted common OR, 1.98 [95% CI, 1.35-2.92]; P < .001). The treatment benefit of IVT was greater in patients with partial reperfusion compared with failed reperfusion. There was no difference in symptomatic intracranial hemorrhages between treatment groups (AOR, 0.71 [95% CI, 0.29-1.81]; P = .45), while the death rate was lower after IVT plus MT (AOR, 0.54 [95% CI, 0.34-0.86]; P = .01). CONCLUSIONS AND RELEVANCE These findings suggest that prior IVT was safe and improved functional outcomes at 90 days. Partial reperfusion was associated with a greater treatment benefit of IVT, indicating a positive interaction between IVT and MT. These results support current guidelines that all eligible patients with stroke should receive IVT before MT and add a new perspective to the debate on noninferiority of combined stroke treatment

An endogenous nanomineral chaperones luminal antigen and peptidoglycan to intestinal immune cells.

In humans and other mammals it is known that calcium and phosphate ions are secreted from the distal small intestine into the lumen. However, why this secretion occurs is unclear. Here, we show that the process leads to the formation of amorphous magnesium-substituted calcium phosphate nanoparticles that trap soluble macromolecules, such as bacterial peptidoglycan and orally fed protein antigens, in the lumen and transport them to immune cells of the intestinal tissue. The macromolecule-containing nanoparticles utilize epithelial M cells to enter Peyer's patches, small areas of the intestine concentrated with particle-scavenging immune cells. In wild-type mice, intestinal immune cells containing these naturally formed nanoparticles expressed the immune tolerance-associated molecule 'programmed death-ligand 1', whereas in NOD1/2 double knockout mice, which cannot recognize peptidoglycan, programmed death-ligand 1 was undetected. Our results explain a role for constitutively formed calcium phosphate nanoparticles in the gut lumen and show how this helps to shape intestinal immune homeostasis

Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models

We study two state of the art deep generative networks, the Introspective Variational Autoencoder and the Style-Based Generative Adversarial Network, for the generation of new diffusion-weighted magnetic resonance images. We show that high quality, diverse and realistic-looking images, as evaluated by external neuroradiologists blinded to the whole study, can be synthesized using these deep generative models. We evaluate diverse metrics with respect to quality and diversity of the generated synthetic brain images. These findings show that generative models could qualify as a method for data augmentation in the medical field, where access to large image database is in many aspects restricted.ISSN:0730-725XISSN:1873-589