English

OpenHI2 -- Open source histopathological image platform

Quantitative Methods 2020-01-16 v1 Computer Vision and Pattern Recognition Networking and Internet Architecture Image and Video Processing

Abstract

Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the study area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats; ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges so that OpenHI2 could be used as a platform for histopathological images. Further addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at https://gitlab.com/BioAI/OpenHI.

Keywords

Cite

@article{arxiv.2001.05158,
  title  = {OpenHI2 -- Open source histopathological image platform},
  author = {Pargorn Puttapirat and Haichuan Zhang and Jingyi Deng and Yuxin Dong and Jiangbo Shi and Hongyu He and Zeyu Gao and Chunbao Wang and Xiangrong Zhang and Chen Li},
  journal= {arXiv preprint arXiv:2001.05158},
  year   = {2020}
}

Comments

Preprint version accepted to AIPath2019 workshop at BIBM2019. 6 pages, 3 figures, 2 tables

R2 v1 2026-06-23T13:11:37.280Z