Paper Info
Title
Openhi - An Open Source Framework For Annotating Histopathological Image
Abstract
Histopathological images carry informative cellular visual phenotypes and have been digitalized in huge amount in medical institutes. However, the lack of software for annotating the specialized images has been a hurdle of fully exploiting the images for educating and researching, and enabling intelligent systems for automatic diagnosis or phenotype-genotype association study. This paper proposes an open-source web framework, OpenHI, for the whole-slide image annotation. The proposed framework could be utilized for simultaneous collaborative or crowd-sourcing annotation with standardized semantic enrichment at a pixel-level precision. Meanwhile, our accurate virtual magnification indicator provides annotators a crucial reference for deciding the grading of each region. In testing, the framework can responsively annotate the acquired whole-slide images from TCGA project and provide efficient annotation which is precise and semantically meaningful. OpenHI is an open-source framework thus it can be extended to support the annotation of whole-slide images from different source with different oncological types. It is publicly available at https://gitlab.com/BioAI/OpenHI/. The framework may facilitate the creation of large-scale precisely annotated histopathological image datasets.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621393
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Field
DocType
ISSN
Annotation,Automatic image annotation,Information retrieval,Intelligent decision support system,Computer science,Web application framework,Software,Artificial intelligence,Machine learning
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Pargorn Puttapirat100.68
Haichuan Zhang202.70
Yuchen Lian300.34
Chunbao Wang413.75
Xiangrong Zhang549348.70
Lixia Yao6459.63
Chen Li703.72