Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks.
Lung cancer is one of the most diagnosable forms of cancer worldwide. The early diagnoses of pulmonary nodules in computed tomography (CT) chest scans are crucial for potential patients. Recent researches have showed that the methods based on deep learning have made a significant progress for the me...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 7; pp. 1567 - 1581 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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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=137162500&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137162500 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2019 vid: 57 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137162500 137162500 NLM31025248 137162500 10.1007/s11517-019-01976-1 NLM31025248 137162500 ppf: 1567 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. aug: au: Chen, Genlang Zhang, Jiajian Zhuo, Deyun Pan, Yuning Pang, Chaoyi affil: Ningbo Institute of Technology, Zhejiang University, 315100, Ningbo, China sug: subj: Lung Neoplasms Imaging, Three-Dimensional Methods Tomography, X-Ray Computed Methods Algorithms Sensitivity and Specificity Solitary Pulmonary Nodule Resource Databases Lung Neoplasms Pathology Human Funding Source ab: Lung cancer is one of the most diagnosable forms of cancer worldwide. The early diagnoses of pulmonary nodules in computed tomography (CT) chest scans are crucial for potential patients. Recent researches have showed that the methods based on deep learning have made a significant progress for the medical diagnoses. However, the achievements on identification of pulmonary nodules are not yet satisfactory enough to be adopted in clinical practice. It is largely caused by either the existence of many false positives or the heavy time of processing. With the development of fully convolutional networks (FCNs), in this study, we proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from their environments and then removes the false positives. Especially, it optimizes the network architecture for the identification of nodules rapidly and accurately. In order to remove the false positives, the suspected nodules are reduced using the 2D models. Furthermore, according to the significant differences between nodules and non-nodules in 3D shapes, the false positives are eliminated by integrating into the 3D models and classified via 3D CNNs. The experiments on 1000 patients indicate that our proposed method achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s. Graphical Abstract This paper has proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from CT images and removes the false positives. As shown in the above, the proposed approach consists of three stages. In stage I, raw data are filtered and normalized. The clean normalized data are then segmented in stage II to extract the suspected nodular lesions through 2D FCNs. Stage III is to remove some false positives generated at stage II via 3D CNNs and outputs the final results. The experiments on 1000 patients indicate that our proposed method has achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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