Abstract
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.
Highlights
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies
We describe an end-to-end pipeline for image processing, a custom web interface for rapid expert annotation, and training of Convolutional neural networks (CNNs) models that result in high performance multi-task classifiers capable of distinguishing Aβ pathologies in the form of cored plaques, diffuse plaques, and cerebral amyloid angiopathy (CAA)
We found that 43 digitized glass microscope slides (WSIs, see Supplementary Table 1 for case details) used in this study yielded over 500,000 individual candidate objects of interest
Summary
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. The potential for neuropathologic deep phenotyping efforts that account for anatomic location, diverse sources of proteinopathy, and quantitative pathology densities motivates the development of effective and scalable quantitative methods to differentiate pathological subtypes[17,18,19]. Existing quantitative methods, such as positive pixel count[20] algorithms, typically rely on human-defined[21] red-green-blue (RGB) or hue-saturation-value (HSV) ranges (i.e., pixel color and intensity) and are sensitive to batch differences or to the variable effects of formalin fixation on tinctorial properties. Deep learning has transformed medical image analysis[25,26]
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