Title: OpenHI: open platform for histopathological image annotation

Authors: Pargorn Puttapirat; Haichuan Zhang; Jingyi Deng; Yuxin Dong; Jiangbo Shi; Peiliang Lou; Chunbao Wang; Lixia Yao; Xiangrong Zhang; Chen Li

Addresses: Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Department of Software Engineering, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China ' Department of Pathology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China ' Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA ' Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China ' Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China

Abstract: Consolidating semantically rich annotation on digital histopathological images known as whole-slide images requires a software capable of handling such type of biomedical data with support for procedures which align with existing pathological protocols. Demands for large-scale annotated histopathological datasets are on the raise since they are needed for developments of artificial intelligence techniques to promote automated diagnosis, mass screening, phenotype-genotype association study, etc. This paper presents an open platform for efficient collaborative histopathological image annotation with standardised semantic enrichment at a pixel-level precision named OpenHI (Open Histopathological Image). The framework's responsive processing algorithm can perform large-scale histopathological image annotation and serve as biomedical data infrastructure for digital pathology. Its web-based design is highly configurable and could be extended to annotate histopathological image of various oncological types. The framework is open-source and fully documented.

Keywords: OpenHI; digital pathology; WSI; whole-slide image; image annotation; virtual slide; virtual magnification; histopathology; cancer diagnosis; cancer grading; genotype-phenotype association.

DOI: 10.1504/IJDMB.2019.101393

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.4, pp.328 - 349

Received: 22 May 2019
Accepted: 23 May 2019

Published online: 05 Aug 2019 *

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