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@article{2290959,
author = {Horák, Jiří and Furmanová, Katarína and Kozlíková, Barbora and Brázdil, Tomáš and Holub, Petr and Kačenga, Martin and Gallo, Matej and Nenutil, Rudolf and Byška, Jan and Rusňák, Vít},
article_location = {England},
article_number = {3},
doi = {http://dx.doi.org/10.1111/cgf.14812},
keywords = {Medical Imaging; Scientific Visualization; Open Pathology; Toolkit; artificial intelligence; Visual Analysis; AI explainability; GPU Rendering;},
language = {eng},
issn = {0167-7055},
journal = {COMPUTER GRAPHICS FORUM},
title = {xOpat: eXplainable Open Pathology Analysis Tool},
url = {https://diglib.eg.org/handle/10.1111/cgf14812},
volume = {42},
year = {2023}
}
TY - JOUR
ID - 2290959
AU - Horák, Jiří - Furmanová, Katarína - Kozlíková, Barbora - Brázdil, Tomáš - Holub, Petr - Kačenga, Martin - Gallo, Matej - Nenutil, Rudolf - Byška, Jan - Rusňák, Vít
PY - 2023
TI - xOpat: eXplainable Open Pathology Analysis Tool
JF - COMPUTER GRAPHICS FORUM
VL - 42
IS - 3
SP - 63-73
EP - 63-73
PB - Wiley
SN - 01677055
KW - Medical Imaging
KW - Scientific Visualization
KW - Open Pathology
KW - Toolkit
KW - artificial intelligence
KW - Visual Analysis
KW - AI explainability
KW - GPU Rendering;
UR - https://diglib.eg.org/handle/10.1111/cgf14812
N2 - 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, browser- based 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.
ER -
HORÁK, Jiří, Katarína FURMANOVÁ, Barbora KOZLÍKOVÁ, Tomáš BRÁZDIL, Petr HOLUB, Martin KAČENGA, Matej GALLO, Rudolf NENUTIL, Jan BYŠKA and Vít RUSŇÁK. xOpat: eXplainable Open Pathology Analysis Tool. \textit{COMPUTER GRAPHICS FORUM}. England: Wiley, 2023, vol.~42, No~3, p.~63-73. ISSN~0167-7055. Available from: https://dx.doi.org/10.1111/cgf.14812.
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