Curadiomics: A GPU-based radiomics feature extraction toolkit

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

doi:10.1007/978-3-030-40124-5_5

Curadiomics: A GPU-based radiomics feature extraction toolkit

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

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 language	English
Title of host publication	Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings
Editors	Hassan Mohy-ud-Din, Saima Rathore
Publisher	Springer
Pages	44-52
Number of pages	9
ISBN (Print)	9783030401238
DOIs	https://doi.org/10.1007/978-3-030-40124-5_5
Publication status	Published - 2020
Externally published	Yes
Event	1st 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 13 → 2019 Oct 13

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11991 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st 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
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/13

Bibliographical note

Funding Information:
This research was supported by the grants from the National Key Research and Development Program of China (No. 2017YFC0107602 and No. 2018YFC0116400), Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (ZH2018QNA67).

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

CUDA
Feature extraction
GPU
Radiomics

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-40124-5_5

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

Curadiomics: A GPU-based radiomics feature extraction toolkit. / Jiao, Yining; Ijurra, Oihane Mayo; Zhang, Lichi et al.
Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings. ed. / Hassan Mohy-ud-Din; Saima Rathore. Springer, 2020. p. 44-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11991 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jiao, Y, Ijurra, OM, 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11991 LNCS, Springer, pp. 44-52, 1st 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, 19/10/13. https://doi.org/10.1007/978-3-030-40124-5_5

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

Jiao, Yining ; Ijurra, Oihane Mayo ; Zhang, Lichi et al. / Curadiomics : A GPU-based radiomics feature extraction toolkit. Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings. editor / Hassan Mohy-ud-Din ; Saima Rathore. Springer, 2020. pp. 44-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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AU - Jiao, Yining

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AU - Shen, Dinggang

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N1 - Funding Information: This research was supported by the grants from the National Key Research and Development Program of China (No. 2017YFC0107602 and No. 2018YFC0116400), Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (ZH2018QNA67). Publisher Copyright: © Springer Nature Switzerland AG 2020.

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PB - Springer

Y2 - 13 October 2019 through 13 October 2019

ER -

Curadiomics: A GPU-based radiomics feature extraction toolkit

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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