Towards the Automatization of Cranial Implant Design in Cranioplasty II: Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings: Lecture Notes in Computer Science, cartea 13123
Editat de Jianning Li, Jan Eggeren Limba Engleză Paperback – 4 dec 2021
The 7 papers are presented together with one invited paper, one qualitative evaluation criteria from neurosurgeons and a dataset descriptor. This challenge aims to provide more affordable, faster, and more patient-friendly solutions to the design and manufacturing of medical implants, including cranial implants, which is needed in order to repair a defective skull from a brain tumor surgery or trauma. The presented solutions can serve as a good benchmark for future publications regarding 3D volumetric shape learning and cranial implant design.
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Specificații
ISBN-13: 9783030926519
ISBN-10: 3030926516
Pagini: 129
Ilustrații: IX, 129 p. 76 illus., 67 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.2 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
ISBN-10: 3030926516
Pagini: 129
Ilustrații: IX, 129 p. 76 illus., 67 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.2 kg
Ediția:1st ed. 2021
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
Personalized Calvarial Reconstruction in Neurosurgery.- Qualitative Criteria for Designing Feasible Cranial Implants.- Segmentation of Defective Skulls from CT Data for Tissue Modelling.- Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets.- Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation.- A U-Net based System for Cranial Implant Design with Pre-processing and Learned Implant Filtering.- Sparse Convolutional Neural Network for Skull Reconstruction.- Cranial Implant Prediction by Learning an Ensemble of Slice-based Skull Completion networks.- PCA-Skull: 3D Skull Shape Modelling Using Principal Component Analysis.- Cranial Implant Design using V-Net based Region of Interest Reconstruction.