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Deformable image registration of abdominal CT images using deep learning: challenges and opportunities

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If you have a question about this talk, please contact J.W.Stevens .

In the past few years, a variety of different methods have been proposed to use deep learning for deformable medical image registration. However, certain challenges need to be overcome before such methods can be readily used in clinical settings. For example, how do these methods deal with small amounts of training data or challenging registrations? Which characteristics of displacement fields are prohibitive when using deep learning? And which image similarity metrics and evaluation methods are most informative when quantifying the performance of such methods? This lecture will demonstrate how such questions may be answered using a longitudinal dataset of abdominal CT images. In addition, a novel training strategy is proposed based on transfer learning and displacement field simulations.

This talk is part of the CMIH Hub seminar series series.

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Gonville & Caius College, Bateman Auditorium Room 2S3, Donald McIntyre Building, Faculty of Education, 184 Hills Road, Cambridge Donald McIntyre Building, Faculty of Education, 184 Hills Road, Cambridge, room 2S5

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