MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma SegmentationFunding This work is supported by National Natural Science Foundation of China (NSFC 61701324, NSFC 62071314) and Sichuan Science and Technology Program (2020YFG0079).
Background An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task.
Objectives The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility.
Methods In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure).
Results To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images.
Conclusion The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.
Keywordsnasopharyngeal carcinoma - magnetic resonance imaging - random forest - recursive feature selection
All procedures performed in studies involving human participants were in accordance with the Ethical Standards of the Institutional and/or National Research Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Received: 26 April 2020
Accepted: 24 November 2020
22 February 2021 (online)
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- 1 Chu EA, Wu JM, Tunkel DE, Ishman SL. Nasopharyngeal carcinoma: the role of the Epstein-Barr virus. Medscape J Med 2008; 10 (07) 165
- 2 Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68 (06) 394-424
- 3 Klein G. Epstein-Barr virus, infectious mononucleosis, Burkitt's lymphoma and nasopharyngeal carcinoma. Isr J Med Sci 1977; 13 (07) 716-724
- 4 Chua MLK, Wee JTS, Hui EP, Chan ATC. Nasopharyngeal carcinoma. Lancet 2016; 387 (10022): 1012-1024
- 5 Mohammed MA, Abd Ghani MK, Hamed RI. et al. Analysis of an electronic methods for nasopharyngeal carcinoma: prevalence, diagnosis, challenges and technologies. J Comput Sci 2017; 21: 241-254
- 6 Mohammed MA, Abd Ghani MK, Hamed RI. et al. Review on nasopharyngeal carcinoma: concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research literature. J Comput Sci 2017; 21: 283-298
- 7 Qin H, Wang R, Wei G. et al. Overexpression of osteopontin promotes cell proliferation and migration in human nasopharyngeal carcinoma and is associated with poor prognosis. Eur Arch Otorhinolaryngol 2018; 275 (02) 525-534
- 8 Peng PJ, Lv BJ, Wang ZH. et al. Multi-institutional prospective study of nedaplatin plus S-1 chemotherapy in recurrent and metastatic nasopharyngeal carcinoma patients after failure of platinum-containing regimens. Ther Adv Med Oncol 2017; 9 (02) 68-74
- 9 Siva Sankar P, Che Mat MF, Muniandy K. et al. Modeling nasopharyngeal carcinoma in three dimensions. Oncol Lett 2017; 13 (04) 2034-2044
- 10 Abd Ghani MK, Mohammed MA, Arunkumar N. et al. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput Appl 2020; 32: 625-638
- 11 Zhou J, Chan KL, Xu P. et al. Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine. Paper presented at: Proceeding of IEEE International Symposium on Biomedical Imaging: Nano to Macro; Arlington, VA, 2006: 1364-1367
- 12 Tatanun C, Ritthipravat P, Bhongmakapat T. et al. Automatic segmentation of nasopharyngeal carcinoma from CT images: region growing based technique. Paper presented at: Proceedings of the Biomedical Engineering and Informatics (BEMI); 2008: 18-22
- 13 Chanapai W, Bhongmakapat T, Tuntiyatorn L, Ritthipravat P. Nasopharyngeal carcinoma segmentation using a region growing technique. Int J CARS 2012; 7 (03) 413-422
- 14 Wang Y, Zhou L, Yu B. et al. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans Med Imaging 2019; 38 (06) 1328-1339
- 15 Cardenas CE, Yang J, Anderson BM, Court LE, Brock KB. Advances in auto-segmentation. Semin Radiat Oncol 2019; 29 (03) 185-197
- 16 Wang Y, Yu B, Wang L. et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage 2018; 174: 550-562
- 17 Liu X, Zeng Z. A new automatic mass detection method for breast cancer with false positive reduction. Neurocomputing 2017; 152: 388-402
- 18 Pan X, Li L, Yang H. et al. Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 2017; 229 (C): 88-99
- 19 Hussain S, Anwar SM, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 2018; 282 (22) 248-261
- 20 Mohammed MA, Abd Ghani MK, Hamed RI. et al. Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma. J Comput Sci 2017; 21: 263-274
- 21 Mohammed MA, Abd Ghani MK, Arunkumar N. et al. Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput 2020; 76: 1086-1104
- 22 Breiman L. Manual on Setting Up, Using, and Understanding Random Forests V3.1. 2002: 29 . Accessed May 5, 2012 at: http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf
- 23 Breiman L. Bagging predictors. Mach Learn 1996; 24 (02) 123-140
- 24 Breiman L. Random forests. Mach Learn 2001; 45 (01) 5-32
- 25 Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003; 43 (06) 1947-1958
- 26 de Santana FB, Mazivila SJ, Gontijo LC, Neto WB, Poppi RJ. Discrimination between authentic and adulterated andiroba oil using FTIR-HATR spectroscopy and random forest. Food Anal Methods 2018; 11: 1927-1935
- 27 Kawakubo H, Yoshida H. Rapid feature selection based on random forests for high-dimensional data. Paper presented at: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA); Athens, Greece, 2012: 1-7
- 28 Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. Alzheimer's Disease Neuroimaging Initiative. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage 2013; 65: 167-175
- 29 Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2012; 46 (1–3): 389-422
- 30 Granitto PM, Furlanello C, Biasioli F, Gasperi F. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom Intell Lab Syst 2006; 83 (02) 83-90
- 31 Nanni L, Lumini A, Brahnam S. Survey on LBP based texture descriptors for image classification. Expert Syst Appl 2002; 39 (03) 3634-3641
- 32 Ma Z, Wu X, Sun S. et al. A discriminative learning based approach for automated nasopharyngeal carcinoma segmentation leveraging multi-modality similarity metric learning, IEEE. ISBI 2018; 813-816
- 33 Liaw A, Wiener M. Classification and regression by random forest. R News 2002; 2 (03) 18-22
- 34 Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinform 2018; 9 (01) 307
- 35 Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics 2010; 26 (10) 1340-1347
- 36 Bouhamed H, Lecroq T, Rebaï A. New filter method for categorical variables selection. Int. J. Comput. Sci. Issue 2012; 9 (03) 10-19
- 37 Kabir MM, Islam MM, Murase K. A new wrapper feature selection approach using neural network. Neurocomputing 2010; 73 (16–18): 3273-3283
- 38 Zhu Z, Ong YS, Dash M. Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern B Cybern 2007; 37 (01) 70-76
- 39 Talavera L. An evaluation of filter and wrapper methods for feature selection in categorical clustering. Paper presented at: International Symposium of Intelligent Data Analysis; Madrid, Spain, 2005: 440-451
- 40 Wang Y, Zu C, Hu G. et al. Automatic tumor segmentation with deep convolutional neural networks for radiotherapy applications. Neural Process Lett 2018; 48 (03) 1323-1334
- 41 Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. U-net: Convolutional networks for biomedical image segmentation. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention; Munich, Germany, 2015: 234-241
- 42 Yushkevich PA, Gao Y, Gerig G. ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. Paper presented at: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL: 2016: 3342-3345