Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database.
Objectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC's four-fold double reading process.Methods: The LIDC/...
| Publicado en: | European Radiology Vol. 26; no. 7; pp. 2139 - 2148 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2016
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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=116101225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116101225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2016 vid: 26 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116101225 116101225 NLM26443601 116101225 10.1007/s00330-015-4030-7 NLM26443601 PMC4902840 116101225 ppf: 2139 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database. aug: au: Jacobs, Colin Rikxoort, Eva Murphy, Keelin Prokop, Mathias Schaefer-Prokop, Cornelia Ginneken, Bram van Rikxoort, Eva M Schaefer-Prokop, Cornelia M van Ginneken, Bram affil: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen The Netherlands sug: subj: Tomography, X-Ray Computed Methods Resource Databases Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Methods Solitary Pulmonary Nodule Sensitivity and Specificity Lung Diagnosis, Computer Assisted Methods Human Reproducibility of Results Retrospective Design Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools ab: Objectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC's four-fold double reading process.Methods: The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system.Results: The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study.Conclusions: On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process.Key Points: • CAD systems should be validated on public, heterogeneous databases. • The LIDC/IDRI database is an excellent database for benchmarking nodule CAD. • CAD can identify the majority of pulmonary nodules at a low false positive rate. • CAD can identify nodules missed by an extensive two-stage annotation process. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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