Int J Comput Assist Radiol Surg - Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines.

Tópicos

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Resumo

RPOSE: The goal is to automatically detect anomalous vascular cross-sections to attract the radiologist's attention to possible lesions and thus reduce the time spent to analyze the image volume.MATERIALS AND METHODS: We assume that both lesions and calcifications can be considered as local outliers compared to a normal cross-section. Our approach uses an intensity metric within a machine learning scheme to differentiate normal and abnormal cross-sections. It is formulated as a Density Level Detection problem and solved using a Support Vector Machine (DLD-SVM). The method has been evaluated on 42 synthetic phantoms and on 9 coronary CT data sets annotated by 2 experts.RESULTS: The specificity of the method was 97.57% on synthetic data, and 86.01% on real data, while its sensitivity was 82.19 and 81.23%, respectively. The agreement with the observers, measured by the kappa coefficient, was substantial (?=?0.72). After the learning stage, which is performed off-line, the average processing time was within 10 s per artery.CONCLUSIONS: To our knowledge, this is the first attempt to use the DLD-SVM approach to detect vascular abnormalities. Good specificity, sensitivity and agreement with experts, as well as a short processing time, show that our method can facilitate medical diagnosis and reduce evaluation time by attracting the reader's attention to suspect regions.

Resumo Limpo

rpose goal automat detect anomal vascular crosssect attract radiologist attent possibl lesion thus reduc time spent analyz imag volumemateri method assum lesion calcif can consid local outlier compar normal crosssect approach use intens metric within machin learn scheme differenti normal abnorm crosssect formul densiti level detect problem solv use support vector machin dldsvm method evalu synthet phantom coronari ct data set annot expertsresult specif method synthet data real data sensit respect agreement observ measur kappa coeffici substanti learn stage perform offlin averag process time within s per arteryconclus knowledg first attempt use dldsvm approach detect vascular abnorm good specif sensit agreement expert well short process time show method can facilit medic diagnosi reduc evalu time attract reader attent suspect region

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