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Abstract #2436

A Comparative Study of Deep Learning Based Deformable Image Registration Techniques

Soumick Chatterjee1,2,3, Himanshi Bajaj3, Suraj Bangalore Shashidhar3, Sanjeeth Busnur Indushekar3, Steve Simon3, Istiyak Hossain Siddiquee3, Nandish Bandi Subbarayappa3, Oliver Speck1,4,5,6, and Andreas N├╝rnberger2,3,6
1Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 4German Centre for Neurodegenerative Diseases, Magdeburg, Germany, 5Leibniz Institute for Neurobiology, Magdeburg, Germany, 6Center for Behavioral Brain Sciences, Magdeburg, Germany

Deep learning algorithms have been used extensively in tackling medical image registration issues. However, these methods have not thoroughly evaluated on datasets representing real clinic scenarios. Hence in this survey, three state-of-the-art methods were compared against the gold standards ANTs and FSL, for performing deformable image registrations on publicly available IXI dataset, which resembles clinical data. The comparisons were performed for intermodality and intramodality registration tasks; though in all the respective papers, only the intermodality registrations were exhibited. The experiments have shown that for intramodality tasks, all the methods performed reasonably well and for intermodality tasks the methods faced difficulties.

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