Int J Comput Assist Radiol Surg - Compression fracture diagnosis in lumbar: a clinical CAD system.

Tópicos

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Resumo

RPOSE: Lower back pain affects 80-90 % of all people at some point during their life time, and it is considered as the second most neurological ailment after headache. It is caused by defects in the discs, vertebrae, or the soft tissues. Radiologists perform diagnosis mainly from X-ray radiographs, MRI, or CT depending on the target organ. Vertebra fracture is usually diagnosed from X-ray radiographs or CT depending on the available technology. In this paper, we propose a fully automated Computer-Aided Diagnosis System (CAD) for the diagnosis of vertebra wedge compression fracture from CT images that integrates within the clinical routine.METHODS: We perform vertebrae localization and labeling, segment the vertebrae, and then diagnose each vertebra. We perform labeling and segmentation via coordinated system that consists of an Active Shape Model and a Gradient Vector Flow Active Contours (GVF-Snake). We propose a set of clinically motivated features that distinguish the fractured vertebra. We provide two machine learning solutions that utilize our features including a supervised learner (Neural Networks (NN)) and an unsupervised learner (K-Means).RESULTS: We validate our method on a set of fifty (thirty abnormal) Computed Tomography (CT) cases obtained from our collaborating radiology center. Our diagnosis detection accuracy using NN is 93.2 % on average while we obtained 98 % diagnosis accuracy using K-Means. Our K-Means resulted in a specificity of 87.5 % and sensitivity over 99 %.CONCLUSIONS: We presented a fully automated CAD system that seamlessly integrates within the clinical work flow of the radiologist. Our clinically motivated features resulted in a great performance of both the supervised and unsupervised learners that we utilize to validate our CAD system. Our CAD system results are promising to serve in clinical applications after extensive validation.

Resumo Limpo

rpose lower back pain affect peopl point life time consid second neurolog ailment headach caus defect disc vertebra soft tissu radiologist perform diagnosi main xray radiograph mri ct depend target organ vertebra fractur usual diagnos xray radiograph ct depend avail technolog paper propos fulli autom computeraid diagnosi system cad diagnosi vertebra wedg compress fractur ct imag integr within clinic routinemethod perform vertebra local label segment vertebra diagnos vertebra perform label segment via coordin system consist activ shape model gradient vector flow activ contour gvfsnake propos set clinic motiv featur distinguish fractur vertebra provid two machin learn solut util featur includ supervis learner neural network nn unsupervis learner kmeansresult valid method set fifti thirti abnorm comput tomographi ct case obtain collabor radiolog center diagnosi detect accuraci use nn averag obtain diagnosi accuraci use kmean kmean result specif sensit conclus present fulli autom cad system seamless integr within clinic work flow radiologist clinic motiv featur result great perform supervis unsupervis learner util valid cad system cad system result promis serv clinic applic extens valid

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