Methods Inf Med 2020; 59(04/05): 151-161
DOI: 10.1055/s-0040-1721791
Original Article

MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation

Yuchen Fei
1  School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China
,
Fengyu Zhang
1  School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China
,
Chen Zu
2  Department of Risk Controlling Research, JD.com, Sichuan, People's Republic of China
,
Mei Hong
1  School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China
,
Xingchen Peng
3  Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
,
Jianghong Xiao
4  Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
,
Xi Wu
5  School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan, People's Republic of China
,
Jiliu Zhou
1  School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China
5  School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan, People's Republic of China
,
Yan Wang
1  School of Computer Science, Sichuan University, Chengdu, Sichuan, People's Republic of China
› Author Affiliations
Funding This work is supported by National Natural Science Foundation of China (NSFC 61701324, NSFC 62071314) and Sichuan Science and Technology Program (2020YFG0079).

Abstract

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.

Ethical Approval

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.




Publication History

Received: 26 April 2020

Accepted: 24 November 2020

Publication Date:
22 February 2021 (online)

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