Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT.
Objectives: To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures.Methods: Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic ver...
| Publicado en: | European Radiology Vol. 24; no. 4; pp. 872 - 881 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2014
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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=104037796&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104037796 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2014 vid: 24 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104037796 NLM24425527 2012499162 10.1007/s00330-013-3089-2 NLM24425527 104037796 ppf: 872 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT. aug: au: Baum, Thomas Bauer, Jan S Klinder, Tobias Dobritz, Martin Rummeny, Ernst J Noël, Peter B Lorenz, Cristian affil: Institut für Radiologie, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, München, Germany, thbaum@gmx.de. sug: subj: Lumbar Vertebrae Injuries Multidetector Computed Tomography Osteoporosis Radiography Spinal Fractures Radiography Thoracic Vertebrae Injuries Aged Algorithms Cross Sectional Studies Female Human Male Middle Age Prevalence Prospective Studies Retrospective Design ROC Curve Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Objectives: To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures.Methods: Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus.Results: Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p < 0.001). Longitudinal changes in APR and MPR were significantly different between vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p < 0.001).Conclusions: This prototype algorithm may support radiologists in reporting currently underdiagnosed osteoporotic vertebral fractures so that appropriate therapy can be initiated.Key Points: • This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. • Osteoporotic vertebral fractures could be automatically detected using this prototype algorithm. • The prototype algorithm helps radiologists to report underdiagnosed osteoporotic vertebral fractures. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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