Reconstruction, Segmentation, and Analysis of Medical Images (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
174
Utgivningsdatum
2017-01-19
Upplaga
1st ed. 2017
Förlag
Springer International Publishing AG
Medarbetare
Zuluaga, Maria A. (ed.), Bhatia, Kanwal (ed.), Kainz, Bernhard (ed.), Moghari, Mehdi H. (ed.), Pace, Danielle F. (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
67 Illustrations, black and white; XI, 174 p. 67 illus.
Dimensioner
234 x 156 x 10 mm
Vikt
272 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783319522791
Reconstruction, Segmentation, and Analysis of Medical Images (häftad)

Reconstruction, Segmentation, and Analysis of Medical Images

First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers

av Maria A Zuluaga, Kanwal Bhatia, Bernhard Kainz, Mehdi H Moghari, Danielle F Pace
Häftad, Engelska, 2017-01-19
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This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016, and the First International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016. The 17 revised regular papers presented in this book were carefully reviewed and selected from a total of 21 submissions. The papers cover following topics: Registration; Reconstruction; Deep learning for heart segmentation; Discrete optimization and probabilistic intensity modeling; Atlas-based strategies; Random forests.
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Fler böcker av författarna

Maria A Zuluaga, Kanwal Bhatia, Bernhard Kainz, Mehdi H Moghari, Danielle F Pace

Innehållsförteckning

Registration.- Reconstruction.- Deep learning for heart segmentation.- Discrete optimization and probabilistic intensity modeling.- Atlas-based strategies.- Random forests.

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