Developing Topics

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.