Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis.
We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of sev...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 6; pp. 704 - 718 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2015
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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=110813329&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110813329 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2015 vid: 28 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110813329 110813329 110813329 10.1007/s10278-015-9774-8 NLM25708891 PMC4636719 110813329 ppf: 704 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis. aug: au: Niehaus, Ron Stan Raicu, Daniela Furst, Jacob Armato, Samuel affil: School of Computing, DePaul University, 243 S. Wabash Avenue Chicago 60604 USA sug: subj: Diagnosis, Computer Assisted Image Interpretation, Computer Assisted Decision Trees Lung Diseases Diagnosis Diagnostic Imaging Tomography, X-Ray Computed Algorithms Validation Studies ROC Curve Paired T-Tests Descriptive Statistics P-Value Sensitivity and Specificity T-Tests Human ab: We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of several shape features increases disproportionately relative to other image features with increasing size of the nodule. Our shaped-based classification results show an area under the receiver operating characteristic (ROC) curve of 0.65 when classifying spiculation for small nodules and an area of 0.91 for large nodules, resulting in a 26 % difference in classification performance using shape features. An analysis of the results illustrates that this change in performance is driven by features that measure boundary complexity, which perform well for large nodules but perform relatively poorly and do no better than other features for small nodules. For large nodules, the roughness of the segmented boundary maps well to the semantic concept of spiculation. For small nodules, measuring directly the complexity of hard segmentations does not yield good results for predicting spiculation due to limits imposed by spatial resolution and the uncertainty in boundary location. Therefore, a wider range of features, including shape, texture, and intensity features, are needed to predict spiculation ratings for small nodules. A further implication is that the efficacy of shape features for a particular classifier used to create computer-aided diagnosis systems depends on the distribution of nodule sizes in the training and testing sets, which may not be consistent across different research studies. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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