IVAR: Interactive visual analytics of radiomics features from large-scale medical images

Lina Yu, Hengle Jiang, Hongfeng Yu, Chi Zhang, Josiah McAllister, Dandan Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Medical imaging enables researchers and practitioner to uncover the characteristics of diseases (e.g., human cancer) in great detail. However, the sheer size of resulting imaging data and the high dimension of derived features become a major challenge in data analysis, diagnosis, and knowledge discovery. We present a novel visual analytics system, named iVAR, targeted at observing the comprehensive quantification of tumor phenotypes by effectively exploring a large number of quantitative image features. Our system is comprised of multiple linked views combining visualization of three-dimensional volumes and tumors reconstructed by computed tomography (CT) images, and a radiomic analysis of high-dimensional features quantifying tumor image intensity, shape and texture, and three non-image clinical features. Thus, it offers insights into the overall distribution of quantitative imaging features and also enables detailed analysis of the relationship between features. We demonstrate our system through use case scenarios on a real-world large-scale CT dataset with lung cancer.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3916-3923
Number of pages8
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

Keywords

  • high-dimensional data
  • interactivity
  • medical images
  • radiomics
  • visual analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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  • Cite this

    Yu, L., Jiang, H., Yu, H., Zhang, C., McAllister, J., & Zheng, D. (2017). IVAR: Interactive visual analytics of radiomics features from large-scale medical images. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 3916-3923). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258398