Shape in Medical Imaging
International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
Published on 3. October 2020
Book
Paperback/Softback
VIII, 156 pages
978-3-030-61055-5 (ISBN)
Description
This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23 rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, but changed to a virtual format due to the COVID-19 pandemic.
The 12 full papers included in this volume were carefully reviewed and selected from 18 submissions. They were organized in topical sections named: methods; learning; and applications.
More details
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
11 s/w Abbildungen, 61 farbige Abbildungen
VIII, 156 p. 72 illus., 61 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 10 mm
Weight
260 gr
ISBN-13
978-3-030-61055-5 (9783030610555)
DOI
10.1007/978-3-030-61056-2
Schweitzer Classification
Other editions
Additional editions
Martin Reuter | Christian Wachinger | Hervé Lombaert
Shape in Medical Imaging
International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
E-Book
10/2020
Springer
€53.49
Available for download
Content
Methods.-
Composition of Transformations in the Registration of Sets of Points or Oriented Points.- Uncertainty reduction in contour-based 3D/2D registration of bone surfaces.- Learning Shape Priors from Pieces.- Bi-invariant Two-Sample Tests in Lie Groups for Shape Analysis.-
Learning.-
Uncertain-DeepSSM: From Images to Probabilistic Shape Models.- D-Net: Siamese based Network for Arbitrarily Oriented Volume Alignment.- A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data From the Osteoarthritis Initiative.- Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.-
Applications.-
Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.- Learning a statistical full spine model from partial observations.- Morphology-based individual vertebrae classification.- Patient Specific Classification of Dental Root Canal and Crown Shape.