Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.
Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography–computed tomography (PET-CT) is a suitable imaging technique to assess thi...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 3; pp. 462 - 471 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
|
| 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=136223492&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223492 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2019 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223492 136223492 136223492 10.1007/s10278-018-00173-0 136223492 ppf: 462 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images. aug: au: Zhao, Lijun Lu, Zixiao Jiang, Jun Zhou, Yujia Wu, Yi Feng, Qianjin affil: School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, China sug: subj: Nasopharyngeal Neoplasms Diagnosis Tomography, Emission-Computed Utilization Tomography, Emission-Computed Methods Automation Neural Networks (Computer) Methods Image Processing, Computer Assisted Human Nasopharyngeal Neoplasms Therapy Radiotherapy Utilization Time Factors Costs and Cost Analysis China Validation Studies ab: Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography–computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|