Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.

Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into the cerebral vasculature and cause strokes. The likelihood of a plaque rupturing is thought to be associated with its composition (i.e. lipid, calcium, hemorrhage and inflammatory cell content) and the mechanical properties of the plaque. Automating and digitizing histopathological images of these plaques into tissue specific (lipid and calcified) regions can help us compare histologic findings to in vivo imaging and thereby enable us to optimize medical treatments or interventions for patients based on the composition of plaques. Lack of public datasets and the hypocellular nature of plaques have made applying deep learning to this task difficult. To address this, we sampled 1944 regions of interests from 323 whole slide images and drastically varied their pixel resolution from [Formula: see text] to [Formula: see text] as we anticipated that varying the pixel resolution of histology images can provide neural networks more 'context' that pathologists also rely on. We were able to train Mask R-CNN using regions of interests with varied pixel resolution, with a [Formula: see text] increase in pixel accuracy versus training with patches. The model achieved F1 scores of [Formula: see text] for calcified regions, [Formula: see text] for lipid core with fibrinous material and cholesterol crystals, and [Formula: see text] for fibrous regions, as well as a pixel accuracy of [Formula: see text]. While the F1 score was not calculated for lumen, qualitative results illustrate the model's ability to predict lumen. Hemorrhage was excluded as a class since only one out of 34 carotid endarterectomy specimens had sufficient hemorrhage for annotation.