Share：
Share this content in WeChat
X
[Chinese][PDF]90078
• Technical Article •
Implement of radiomics flow based on the YAP pipeline
LI Dong-bao  SONG Yang  LUO Qing  XIE Hai-bin  YANG Guang 

DOI:10.12015/issn.1674-8034.2018.07.009.


[Abstract] Objective: To support radiomics studies based YAP (Yet Another Pipeline), which is originally a framework for magnetic resonance image reconstruction and post-processing.Materials and Methods: We introduced support of Python into YAP pipeline, so that processors in the pipeline can be programmed in Python. Then we implemented a radiomics pipeline with PyRadiomics package. Finally, the pipeline was used to study brain tumor grading problem with BRATS2017 open datasets.Results: A complete radiomics pipeline was built, which involved hybrid programming of C++ and Python. Best results for BRATS2017 tumor grading were achieved when 12 features were selected, with the best accuracy of 94.5% and AUC (Area Under Curve) for receiver operating characteristic curve of 0.9650.Conclusions: Hybrid programming of Python and C++, together with the facilities provided by YAP framework, may facilitate radiomics studies.
[Keywords] Image processing, computer-assisted；Magnetic resonance imaging；Programming languages；Medicine

LI Dong-bao Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China normal University, Shanghai 200062, China

SONG Yang Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China normal University, Shanghai 200062, China

LUO Qing Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China normal University, Shanghai 200062, China

XIE Hai-bin* Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China normal University, Shanghai 200062, China; Shanghai Kangda Colorful Medical Technology Co., Ltd., Shanghai 200062, China

YANG Guang* Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China normal University, Shanghai 200062, China; Shanghai Kangda Colorful Medical Technology Co., Ltd., Shanghai 200062, China

*Correspondence to: Xie HB, E-mail: hbxie@phy.ecnu.edu.cn. Yang G, E-mail: gyang@phy.ecnu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  This research was supported by Key Program of National Natural Science Foundation of China No. 61731009
Received  2018-04-13
Accepted  2018-05-20
DOI: 10.12015/issn.1674-8034.2018.07.009
DOI:10.12015/issn.1674-8034.2018.07.009.

[1]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
[2]
Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
[3]
Zhang X, Xu XP, Lu HB. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging, 2017, 46(5): 1281-1288.
[4]
Bickelhaupt S, Paech D, Kickingereder P, et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging. 2017, 46(2): 604-616.
[5]
Xu X, Liu Y, Zhang X, et al. Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps. Abdom Radiol (NY), 2017, 42(7): 1896-1905.
[6]
罗庆，张成秀，李文静，等.一种新的磁共振图像处理流水线的设计与实现．波谱学杂志, 2018, 35(1): 40-51
[7]
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res, 2017, 77(21): e104-e107.
[8]
Cook GR, Siddique M, Taylor BP, et al. Radiomics in PET: principles and applications. Clin Transl Imaging, 2014, 2(3): 269-276.
[9]
Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
[10]
Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. systems, man and cybernetics. IEEE Transactions Systems Man Cybernetic, 1973, 3(6): 610-621.
[11]
Thibault G, Fertil B, Navarro C, et al. Texture indexes and gray level size zone matrix. application to cell nuclei classification. Pattern Recogn Inform Proces (PRIP), 2009: 140-145.
[12]
Tang X. Texture information in run-length matrices. IEEE Transactions Image Proces, 1998, 7(11): 1602-1609.
[13]
Zwanenburg A, Leger S, Vallières M, et al. Image biomarker standardisation initiative: feature definitions. Computer Vision Pattern Recognition, 2016: 1612.
[14]
Amadasun M, King R. Textural features corresponding to textural properties. IEEE Transactions Systems Man Cybernetics, 1989, 19(5): 1264-1274.
[15]
Khalvati F, Wong A, Haider M. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Medical Imaging, 2015, 15: 27.
[16]
Galton F. Regression towards mediocrity in hereditary stature. J Anthropological Instit Br, 1886, 15: 246-263.
[17]
EibenP AE, RauéZs PE, Ruttkay ZS. Genetic algorithms with multi-parent recombination. Lecture Notes Computer Science, 1994, 866: 78-87.
[18]
De Martino F, Valente G, Staeren N, et al. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuro Image, 2008, 43(1): 44-58.
[19]
Cortes C, Vapnik, V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.
[20]
Stephen Cass, The 2017 Top Programming Languages: Python jumps to No. 1, and Swift enters the Top Ten. IEEE Spectrum, 2017: 24.
[21]
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions Med Imaging.. 2015, 34(10):1993-2024.

PREV The value of MRI intravoxel incoherent motion imaging (IVIM) and diffusion kurtosis imaging (DKI) in the differential diagnosis of benign and malignant bone and soft tissue tumors of lower extremity
NEXT Research progresses of MRI in white matter hyperintensity
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn