Openhi: Open platform for histopathological image annotation

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)328-349
Number of pages22
JournalInternational Journal of Data Mining and Bioinformatics
Issue number4
StatePublished - 2019


  • Cancer diagnosis
  • Cancer grading
  • Digital pathology
  • Genotype-phenotype association
  • Histopathology
  • Image annotation
  • OpenHI
  • Virtual magnification
  • Virtual slide
  • WSI
  • Whole-slide image

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences


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