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Shape in Medical Imaging: International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings de Martin Reuter

Shape in Medical Imaging: International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings: Lecture Notes in Computer Science, cartea 12474

Editat de Martin Reuter, Christian Wachinger, Hervé Lombaert, Beatriz Paniagua, Orcun Goksel, Islem Rekik
en Limba Engleză Paperback – 3 oct 2020
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.
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Specificații

ISBN-13: 9783030610555
ISBN-10: 3030610551
Pagini: 156
Ilustrații: VIII, 156 p. 72 illus., 61 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.24 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

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.