Automating Microinfarct Screening in Hematoxylin and Eosin‐stained Human Brain Tissues: A Machine Learning Approach

Abstract Background Microinfarcts, characteristic lesions of vascular dementia (VaD), are heterogenous and vary in appearance, which pose a considerable challenge for VaD grading as there is great interrater variability in microinfarct assessment. We propose a novel application of machine learning (ML) in the automated screening of microinfarcts, addressing a gap in the post‐mortem analysis of VaD in whole slide images (WSIs) from human brain. Method Our study adapts a patch‐based pipeline with convolutional neural networks (CNNs) to automate microinfarct screening in WSIs. We compare performance and computational cost of different fields‐of‐view (FOV) for the patch‐based method. Additionally, we propose a postprocessing step and leverage multiple FOVs to mitigate false positives. This study is validated against 66 annotated WSIs (N = 40 infarcted, N = 26 un‐infarcted) from Frontal, Parietal, and Occipital white matter regions across 22 cases. Annotations are from a single trained expert, who delineated microinfarct regions and graded white matter rarefaction. Result We report screening performance, i.e, the ability to distinguish infarct‐positive from infarct‐negative WSIs, and detection performance, i.e, the ability to localize the microinfarct regions within a WSI. Despite the inherent challenges of the inexact boundaries of microinfarcts, our models demonstrate notable efficacy in screening WSIs for infarcts, with ResNet‐18 achieving 100% accuracy at WSI level. However, the sensitivity in detecting infarct regions is below 50%, based on Intersection over Union thresholds above 20% overlap (a performance metric used to evaluate the accuracy of object detection models). Leveraging multiple FOV improves both detection and screening performances, reducing false positives. Conclusion This work presents a proof‐of‐concept pipeline driven by machine learning to automate microinfarct screening & detection in WSIs. This workflow provides new avenues in the automated analysis of neuropathological images of microinfarcts, bringing potential advancement in ML‐based dementia and neuropathology research.