Shape in Medical Imaging International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings /
Shape in Medical Imaging International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / [electronic resource] :
edited by Martin Reuter, Christian Wachinger, Hervé Lombaert, Beatriz Paniagua, Orcun Goksel, Islem Rekik.
- 1st ed. 2020.
- VIII, 156 p. 72 illus., 61 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12474 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12474 .
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.
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.
9783030610562
10.1007/978-3-030-61056-2 doi
Computer vision.
Application software.
Machine learning.
Computers, Special purpose.
Pattern recognition systems.
Computer Vision.
Computer and Information Systems Applications.
Machine Learning.
Special Purpose and Application-Based Systems.
Automated Pattern Recognition.
TA1634
006.37
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.
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.
9783030610562
10.1007/978-3-030-61056-2 doi
Computer vision.
Application software.
Machine learning.
Computers, Special purpose.
Pattern recognition systems.
Computer Vision.
Computer and Information Systems Applications.
Machine Learning.
Special Purpose and Application-Based Systems.
Automated Pattern Recognition.
TA1634
006.37