xOpat: eXplainable Open Pathology Analysis Tool
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open-source, browserbased WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists' annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol-agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.
Description
CCS Concepts: Human-centered computing -> Visualization systems and tools; Scientific visualization
@article{10.1111:cgf.14812,
journal = {Computer Graphics Forum},
title = {{xOpat: eXplainable Open Pathology Analysis Tool}},
author = {Horák, Jirí and Furmanová, Katarína and Kozlíková, Barbora and Brázdil, Tomáš and Holub, Petr and Kacenga, Martin and Gallo, Matej and Nenutil, Rudolf and Byška, Jan and Rusnak, Vit},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14812}
}