Improving Multi-Atlas Segmentation Methods for Medical Images
Licentiatavhandling, 2017
Supervised learning
semantic segmentation
multi-atlas segmentation
conditional random fields
label fusion
feature-based registration
image registration
random decision forests
convolutional neural networks
medical image segmentation
Författare
Jennifer Alvén
Chalmers, Signaler och system, Signalbehandling och medicinsk teknik
Überatlas: Fast and robust registration for multi-atlas segmentation
Pattern Recognition Letters,;Vol. 80(2016)p. 249-255
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Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography
Journal of Medical Imaging,;Vol. 3(2016)p. Article number 034003-
Artikel i vetenskaplig tidskrift
Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),;Vol. 9127(2015)p. 92-102
Paper i proceeding
Good Features for Reliable Registration in Multi-Atlas Segmentation
Proceedings of the VISCERAL Anatomy3 Segmentation Challenge co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2015),;Vol. 1390(2015)p. 12-17
Paper i proceeding
Shape-aware multi-atlas segmentation
Proceedings - International Conference on Pattern Recognition,;Vol. 0(2016)p. 1101-1106
Paper i proceeding
Alvén, J., Kahl, F., Landgren, M., Larsson, V., Ulén, J., Enqvist, O. Shape-Aware Label Fusion for Multi-Atlas Frameworks.
Styrkeområden
Informations- och kommunikationsteknik
Livsvetenskaper och teknik (2010-2018)
Ämneskategorier
Datorseende och robotik (autonoma system)
Medicinsk bildbehandling
Utgivare
Chalmers
EC, Hörsalsvägen 11, Göteborg
Opponent: Associate Professor Robin Strand (1) Centre for Image Analysis, Division of Visual Information and Interaction, Dept. of Information Technology, Uppsala University (2) Section of Radiology, Dept. of Surgical Sciences, Uppsala University