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Shape in Medical Imaging: International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1st ed. 2020 [Paperback / softback]

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  • Format: Paperback / softback, 156 pages, height x width: 235x155 mm, weight: 454 g, 61 Illustrations, color; 11 Illustrations, black and white; VIII, 156 p. 72 illus., 61 illus. in color., 1 Paperback / softback
  • Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics 12474
  • Pub. Date: 03-Oct-2020
  • Publisher: Springer Nature Switzerland AG
  • ISBN-10: 3030610551
  • ISBN-13: 9783030610555
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Shape in Medical Imaging: International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1st ed. 2020Larger Image
  • Format: Paperback / softback, 156 pages, height x width: 235x155 mm, weight: 454 g, 61 Illustrations, color; 11 Illustrations, black and white; VIII, 156 p. 72 illus., 61 illus. in color., 1 Paperback / softback
  • Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics 12474
  • Pub. Date: 03-Oct-2020
  • Publisher: Springer Nature Switzerland AG
  • ISBN-10: 3030610551
  • ISBN-13: 9783030610555
Other books in subject:
Permanent link: https://www.kriso.ee/db/9783030610555.html
Keywords:
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.
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 Alzheimers 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.