Dynamic ensemble selection of learner-descriptor classifiers to assess curve types in adolescent idiopathic scoliosis.
While classification is important for assessing adolescent idiopathic scoliosis (AIS), it however suffers from low interobserver and intraobserver reliability. Classification using ensemble methods may contribute to improving reliability using the proper 2D and 3D images of spine curvature features....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 12; pp. 2221 - 2232 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2018
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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=133056262&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133056262 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2018 vid: 56 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133056262 133056262 NLM29949021 10.1007/s11517-018-1853-9 NLM29949021 133056262 ppf: 2221 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Dynamic ensemble selection of learner-descriptor classifiers to assess curve types in adolescent idiopathic scoliosis. aug: au: García-Cano, Edgar Arámbula Cosío, Fernando Duong, Luc Bellefleur, Christian Roy-Beaudry, Marjolaine Joncas, Julie Parent, Stefan Labelle, Hubert affil: École de technologie supérieure, 1100 Notre-Dame Street West, H3C 1K3, Montreal, Quebec, Canada sug: subj: Image Processing, Computer Assisted Methods Scoliosis Algorithms Resource Databases Scales ab: While classification is important for assessing adolescent idiopathic scoliosis (AIS), it however suffers from low interobserver and intraobserver reliability. Classification using ensemble methods may contribute to improving reliability using the proper 2D and 3D images of spine curvature features. In this study, we present two new techniques to describe the spine, namely, leave-one-out and fan leave-one-out. Using these techniques, three descriptors are computed from a stereoradiographic 3D reconstruction to describe the relationship between a vertebra and its neighbors. A dynamic ensemble selection method is introduced for automatic spine classification. The performance of the method is evaluated on a dataset containing 962 3D spine models categorized according to three curve types. With a log loss of 0.5623, the dynamic ensemble selection outperforms voting and stacking ensemble learning techniques. This method can improve intraobserver and interobserver reliability, identify the best combination of descriptors for characterizing spine curve types, and provide assistance to clinicians in the form of information to classify borderline curvature types. Graphical abstract ᅟ. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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