J 2023

xOpat: eXplainable Open Pathology Analysis Tool

HORÁK, Jiří, Katarína FURMANOVÁ, Barbora KOZLÍKOVÁ, Tomáš BRÁZDIL, Petr HOLUB et. al.

Basic information

Original name

xOpat: eXplainable Open Pathology Analysis Tool

Authors

HORÁK, Jiří (203 Czech Republic, belonging to the institution), Katarína FURMANOVÁ (703 Slovakia, belonging to the institution), Barbora KOZLÍKOVÁ (203 Czech Republic, belonging to the institution), Tomáš BRÁZDIL (203 Czech Republic, belonging to the institution), Petr HOLUB (203 Czech Republic, belonging to the institution), Martin KAČENGA (703 Slovakia, belonging to the institution), Matej GALLO (703 Slovakia, belonging to the institution), Rudolf NENUTIL (203 Czech Republic), Jan BYŠKA (203 Czech Republic, belonging to the institution) and Vít RUSŇÁK (203 Czech Republic, guarantor, belonging to the institution)

Edition

COMPUTER GRAPHICS FORUM, England, Wiley, 2023, 0167-7055

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 2.500 in 2022

RIV identification code

RIV/00216224:14330/23:00130943

Organization unit

Faculty of Informatics

DOI

UT WoS

001020716600006

Keywords in English

Medical Imaging; Scientific Visualization; Open Pathology; Toolkit; artificial intelligence; Visual Analysis; AI explainability; GPU Rendering;

Tags

Tags

International impact, Reviewed
Změněno: 6/2/2024 21:04, doc. RNDr. Tomáš Brázdil, Ph.D.

Abstract

V originále

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.

Links

LM2018140, research and development project
Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)
Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/1339/2022, interní kód MU
Name: Rozvoj technik pro zpracování dat pro podporu vyhledávání, analýz a vizualizací rozsáhlých datových souborů s využitím umělé inteligence
Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence
824087, interní kód MU
Name: EOSC-Life: Providing an open collaborative space for digital biology in Europe (Acronym: EOSC-Life)
Investor: European Union, RI Research Infrastructures (Excellent Science)
90125, large research infrastructures
Name: BBMRI-CZ III