
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016
19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
Springer (Publisher)
Published on 2. October 2016
Book
Paperback/Softback
XXV, 703 pages
978-3-319-46722-1 (ISBN)
Description
The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR imageanalysis.
More details
Series
Edition
1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
238 s/w Abbildungen
XXV, 703 p. 238 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 40 mm
Weight
1089 gr
ISBN-13
978-3-319-46722-1 (9783319467221)
DOI
10.1007/978-3-319-46723-8
Schweitzer Classification
Other editions
Additional editions

Sebastien Ourselin | Leo Joskowicz | Mert R. Sabuncu
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016
19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
E-Book
10/2016
Springer
€53.49
Available for download
Content
Machine learning and feature selection.- Deep learning in medical imaging.- Applications of machine learning.- Segmentation.- Cell image analysis.