Curadiomics: A GPU-based radiomics feature extraction toolkit

Yining Jiao, Oihane Mayo Ijurra, Lichi Zhang, Dinggang Shen, Qian Wang

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

Abstract

Radiomics is widely-used in imaging based clinical studies as a way of extracting high-throughput image descriptors. However, current tools for extracting radiomics features are generally run on CPU only, which leads to large time consumption in situations such as large datasets or complicated task/method verifications. To address this limitation, we have developed a GPU based toolkit namely cuRadiomics, where the computing time can be significantly reduced. In cuRadiomics, the CUDA-based feature extraction process for two different classes of radiomics features, including 18 first-order features based on intensity histograms and 23 texture features based on gray level cooccurrence matrix (GLCM), has been developed. We have demonstrated the advantage of the cuRadiomics toolkit over CPU-based feature extraction methods using BraTS18 and KiTS19 datasets. For example, regarding the whole image as ROI, feature extraction process using cuRadiomics is 143.13 times faster than that using PyRadiomics. Thus, the potential advantage provided by cuRadiomics enables the radiomics related statistical methods more adaptive and convenient to use than before. Our proposed cuRadiomics toolkit is now publicly available at https://github.com/jiaoyining/cuRadiomics.

Original languageEnglish
Title of host publicationRadiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsHassan Mohy-ud-Din, Saima Rathore
PublisherSpringer
Pages44-52
Number of pages9
ISBN (Print)9783030401238
DOIs
Publication statusPublished - 2020 Jan 1
Externally publishedYes
Event1st International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11991 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/13

Keywords

  • CUDA
  • Feature extraction
  • GPU
  • Radiomics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Jiao, Y., Ijurra, O. M., Zhang, L., Shen, D., & Wang, Q. (2020). Curadiomics: A GPU-based radiomics feature extraction toolkit. In H. Mohy-ud-Din, & S. Rathore (Eds.), Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 44-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11991 LNCS). Springer. https://doi.org/10.1007/978-3-030-40124-5_5