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.
|Title of host publication
|Radiomics and Radiogenomics in Neuro-oncology - 1st International Workshop, RNO-AI 2019, held in Conjunction with MICCAI 2019, Proceedings
|Hassan Mohy-ud-Din, Saima Rathore
|Number of pages
|Published - 2020
|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
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|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
|19/10/13 → 19/10/13
Bibliographical noteFunding 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).
© Springer Nature Switzerland AG 2020.
- Feature extraction
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science