TY - GEN
T1 - Curadiomics
T2 - 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
AU - Jiao, Yining
AU - Ijurra, Oihane Mayo
AU - Zhang, Lichi
AU - Shen, Dinggang
AU - Wang, Qian
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.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - CUDA
KW - Feature extraction
KW - GPU
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85081577619&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-40124-5_5
DO - 10.1007/978-3-030-40124-5_5
M3 - Conference contribution
AN - SCOPUS:85081577619
SN - 9783030401238
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 52
BT - Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Mohy-ud-Din, Hassan
A2 - Rathore, Saima
PB - Springer
Y2 - 13 October 2019 through 13 October 2019
ER -