Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.
Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 678 - 685 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143476525&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476525 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476525 143476525 143476525 10.1007/s10278-019-00301-4 143476525 ppf: 678 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation. aug: au: Singadkar, Ganesh Mahajan, Abhishek Thakur, Meenakshi Talbar, Sanjay affil: Department of Electronics & Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India sug: subj: Neural Networks (Computer) Automation Solitary Pulmonary Nodule Diagnosis Lung Neoplasms Diagnosis Tomography, X-Ray Computed Methods Diagnosis, Computer Assisted Human Image Processing, Computer Assisted Methods Resource Databases ab: Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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