Shape in Medical Imaging

International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
1. Auflage 2020
ISBN: 978-3-030-61055-5
Verlag: Springer International Publishing

Buch, Englisch, Band 12474, 156 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 260 g

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd 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.

Zielgruppe

Research

Autoren/Hrsg.

Reuter, Martin

Wachinger, Christian

Rekik, Islem

Lombaert, Hervé

Paniagua, Beatriz

Goksel, Orcun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Inhaltsverzeichnis

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.