Fine-Scale Histologic Features Estimated at Low Magnification

They simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, they developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image.

The scientists acquired high-resolution (0.25 µm/pixel) digital images of H&E-stained slides from 88 excised breast specimens at ×40 magnification using the Aperio Scanscope XT whole-slide scanner. For each whole-slide image, they selected two regions of interest (ROIs) for analysis, each 800 µm × 800 µm in size, with an effort made to capture epithelium and stroma. To estimate histologic composition from low-magnification images, they developed a model that uses the color of a pixel to surmise its content. By exploiting the spatial relationships between histologic elements, and measuring their individual color properties, they derived axes in hue-saturation-value (HSV) space that can be used to predict the histologic composition of a pixel.

The team analyzed 79 images acquired at ×40 magnification using whole-slide imaging. Images were stored in a proprietary format that enabled direct access to the image at lower resolutions, thereby reducing bandwidth and facilitating rapid loading for viewing and analysis. The investigators reported that reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10, but by utilizing pixel color, this ability was improved at all magnifications.

The authors concluded that multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution and their findings suggest that some of these limitations may be overcome with computational modeling. The study was published on June 18, 2018, in the journal Archives Of Pathology & Laboratory Medicine.

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