MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation.
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 c...
| Publicado en: | Methods of Information in Medicine Vol. 59; no. 4/5; pp. 151 - 162 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148884263&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148884263 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2020 vid: 59 iid: 4/5 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 148884263 148884263 NLM33618420 10.1055/s-0040-1721791 NLM33618420 148884263 ppf: 151 ppct: 11 formats: tig: atl: MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation. aug: au: Fei, Yuchen Zhang, Fengyu Zu, Chen Hong, Mei Peng, Xingchen Xiao, Jianghong Wu, Xi Zhou, Jiliu Wang, Yan affil: School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China sug: subj: Nasopharyngeal Neoplasms Algorithms Magnetic Resonance Imaging Reproducibility of Results Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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