An observer model for quantifying panning artifacts in digital pathology

Ali R. N. Avanaki; Kathryn S. Espig; Albert Xthona; Christian Lanciault; Tom R. L. Kimpe

doi:10.1117/12.2255533

10 March 2017 An observer model for quantifying panning artifacts in digital pathology

Ali R. N. Avanaki, Kathryn S. Espig, Albert Xthona, Christian Lanciault, Tom R. L. Kimpe

Author Affiliations +

Proceedings Volume 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment; 101360O (2017) https://doi.org/10.1117/12.2255533
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Typically, pathologists pan from one region of a slide to another, choosing areas of interest for closer inspection. Due to finite frame rate and imperfect zero-order hold reconstruction (i.e., the non-zero time to reach the target brightness after a change in pixel drive), panning in whole slide images (WSI) cause visual artifacts. It is important to study the impact of such artifacts since research suggests that 49% of navigation is conducted in low-power/overview with digital pathology (Molin et al., Histopathology 2015). In this paper, we explain what types of medical information may be harmed by panning artifacts, propose a method to simulate panning artifacts, and design an observer model to predict the impact of panning artifacts on typical human observers’ performance in basic diagnostically relevant visual tasks. The proposed observer model is based on derivation of perceived object border maps from luminance and chrominance information and may be tuned to account for visual acuity of the human observer to be modeled. Our results suggest that increasing the contrast (e.g., using a wide gamut display) with a slow response panel may not mitigate the panning artifacts which mostly affect visual tasks involving spatial discrimination of objects (e.g., normal vs abnormal structure, cell type and spatial relationships between them, and low-power nuclear morphology), and that the panning artifacts worsen with increasing panning speed. The proposed methods may be used as building blocks in an automatic WSI quality assessment framework.

Conference Presentation

Citation Download Citation

Ali R. N. Avanaki, Kathryn S. Espig, Albert Xthona, Christian Lanciault, and Tom R. L. Kimpe "An observer model for quantifying panning artifacts in digital pathology", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360O (10 March 2017); https://doi.org/10.1117/12.2255533

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