TY - GEN
T1 - OpenHI - An open source framework for annotating histopathological image
AU - Puttapirat, Pargorn
AU - Zhang, Haichuan
AU - Lian, Yuchen
AU - Wang, Chunbao
AU - Zhang, Xiangrong
AU - Yao, Lixia
AU - Li, Chen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062509471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062509471&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621393
DO - 10.1109/BIBM.2018.8621393
M3 - Conference contribution
AN - SCOPUS:85062509471
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1076
EP - 1082
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -