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A computer aided dignostic system for survival analysis after EVAR treatment of EVAR

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Abdominal Aortic Aneurysm (AAA) is a local dilation of the Aorta that occurs between the renal and iliac arteries. Recently developed treatment involves the insertion of a endovascular prosthetic (EVAR), which has the advantage of being a minimally invasive procedure but also requires monitoring to analyze postoperative patient outcomes. The most widespread method for monitoring is computerized axial tomography (CAT) imaging, which allows 3D reconstructions and segmentations of the aorta's lumen of the patient under study. Previously published methods measure the deformation of the aorta between two studies of the same patient using image registration techniques. This paper applies neural network and statistical classifiers to build a predictor of patient survival. The features used for classification are the volume registration quality measures after each of the image registration steps. This system provides the medical team an additional decision support tool.
Rocznik
Tom
Strony
51--58
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • Computational Intelligence Group, University of the Basque Country Donostia, San Sebastián
autor
Bibliografia
  • [1] ABRAHAM A., et al., Hybrid learning machines., Neurocomputing, Vol. 72, No. 13-15, 2009, pp. 2729–2730.
  • [2] BREIMAN L., et al., Random forests, Machine learning, Vol. 45, No. 1, 2001, pp. 5–32.
  • [3] BURGES C.J., et al., A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery, Vol. 2, No. 2, 1998, pp. 121–167.
  • [4] DEMIRCI S., et al., Quantification of Abdominal Aortic Deformation after EVAR, SPIE Medical Imaging, Orlando, Florida, USA, 2009.
  • [5] DERRAC J., et al., A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection, Hybrid Artificial Intelligence Systems, 2010, pp. 557–564.
  • [6] HALL M., et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, Vol. 11, 2009, pp. 10.
  • [7] HAYKIN S., et al., Neural Networks: A comprehensive foundation, Prentice Hall 1999.
  • [8] MACIA I., et al., Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair, Computers in Biology and Medicine, 2011.
  • [9] MAIORA J., et al., Thrombus Change Detection After Endovascular Abdominal Aortic Aneurysm Repair, International Journal of Computer Assisted Radiology and Surgery, Heidelberg, Vol. 5, Suppl. 1, 2010, pp. S15.
  • [10] MATTES J., et al., Quantification of the migration and deformation of abdominal aortic aneurysm stent grafts, Medical Imaging 2006: Image Processing, Vol. 6144, 2006, pp. 61440V.
  • [11] OLABARRIAGA S.D., et al., Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modelling, IEEE Transactions On Medical Imaging, Vol. 24, No. 4, 2005, pp. 477-485.
  • [12] RUMELHART D.E., et al., Learning representations by backpropagating errors, Nature, 1986, pp. 533–536.
  • [13] SUBASIC M., et al., Region-based deformable model for aortic wall segmentation, In: Proc. 3rd Int. Symp. Image and Signal Processing and Analysis ISPA 2003, Vol. 2, 2003, pp. 731–735.
  • [14] WOZNIAK M., et al., Designing fusers on the basis of discriminants–evolutionary and neural methods of training, Hybrid Artificial Intelligence Systems, 2010, pp. 590–597.
  • [15] YANOVSKY I., et al., Comparing registration methods for mapping brain change using tensor-based morphometry, Medical Image Analysis, Vol. 13, No. 5, 2009, pp. 679-700.
  • [16] YUSHKEVICH P.A., et al., User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability, Neuroimage, Vol. 31, No. 3, 2006, pp. 1116–1128.
  • [17] ZHU, S.C., et al., Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 18, No. 9, 1996, pp. 884-900.
  • [18] ZHUGE F., et al., An abdominal aortic aneurysm segmentation method: Level set with region and statistical information, Medical Physics, Vol. 33, No. 5, 2006, pp. 1440-1453.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0025-0005
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