cuRadiomics: A GPU-Based Radiomics Feature Extraction Toolkit
| Autor: | Qian Wang, Yining Jiao, Lichi Zhang, Oihane Mayo Ijurra, Dinggang Shen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science business.industry Feature extraction Process (computing) Pattern recognition Image (mathematics) 03 medical and health sciences Task (computing) CUDA 030104 developmental biology 0302 clinical medicine Radiomics 030220 oncology & carcinogenesis Histogram Artificial intelligence business |
| Zdroj: | Radiomics and Radiogenomics in Neuro-oncology ISBN: 9783030401238 RNO-AI@MICCAI |
| DOI: | 10.1007/978-3-030-40124-5_5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|