首页|National Technical University of Athens Reports Findings in Machine Learning (Ao rta Segmentation in 3D CT Images by Combining Image Processing and Machine Learn ing Techniques)
National Technical University of Athens Reports Findings in Machine Learning (Ao rta Segmentation in 3D CT Images by Combining Image Processing and Machine Learn ing Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report.According to news reporting from Athens,Greece,by NewsR x journalists,research stated,"Aorta segmentation is extremely useful in clini cal practice,allowing the diagnosis of numerous pathologies,such as dissection s,aneurysms and occlusive disease.In such cases,image segmentation is prerequ isite for applying diagnostic algorithms,which in turn allow the prediction of possible complications and enable risk assessment,which is crucial in saving li ves." The news correspondents obtained a quote from the research from the National Tec hnical University of Athens,"The aim of this paper is to present a novel fully automatic 3D segmentation method,which combines basic image processing techniqu es and more advanced machine learning algorithms,for detecting and modelling th e aorta in 3D CT imaging data.An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach,based on a Markov Random Field network.The result of the proposed two-stage segmentatio n process is modelled and visualized.The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D mode ls.The performance of segmentation was evaluated both qualitatively and quantit atively against other commonly used segmentation techniques,in terms of the acc uracy achieved,compared to the actual aorta,which was defined manually by expe rts.The proposed methodology achieved superior segmentation performance,compar ed to all compared segmentation techniques,in terms of the accuracy of the extr acted 3D aortic model."
AthensGreeceEuropeAngiologyAortaCyborgsEmerging TechnologiesHealth and MedicineMachine Learning
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