One-shot Learning Landmarks Detection
| Autor: | Clair Vandersteen, François Patou, Zihao Wang, Hervé Delingette, Nicolas Guevara, Charles Raffaelli |
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| Přispěvatelé: | E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Université Côte d'Azur - Département d'orthophonie (UCA Orthophonie), Oticon Medical / Neurelec, ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
3d registration Landmark Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION One-shot learning Convolutional neural network Landmark matching Learning methods Computer vision [INFO]Computer Science [cs] Artificial intelligence business computer computer.programming_language |
| Zdroj: | Lecture Notes in Computer Science The MICCAI workshop on Data Augmentation, Labeling, and Imperfections The MICCAI workshop on Data Augmentation, Labeling, and Imperfections, Oct 2021, strasbourg, France. pp.163-172 Deep Generative Models, and Data Augmentation, Labelling, and Imperfections ISBN: 9783030882099 DGM4MICCAI/DALI@MICCAI MICCAI 2021-Workshop on Data Augmentation, Labeling, and Imperfections MICCAI 2021-Workshop on Data Augmentation, Labeling, and Imperfections, Oct 2021, strasbourg, France. pp.163-172, ⟨10.1007/978-3-030-88210-5_15⟩ |
| ISSN: | 0302-9743 |
| DOI: | 10.1007/978-3-030-88210-5_15⟩ |
| Popis: | International audience; Landmark detection in medical images is important for many clinical applications. Learning-based landmark detection is successful at solving some problems but it usually requires a large number of annotated datasets for the training stage. In addition, traditional methodsusually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetricimages from a single example based on a one-shot learning method. It involves the iterative training of a shallow convolutional neural network combined with a 3D registration algorithm in order to perform automatic organ localization and landmark matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The results show that our oneshot learning scheme converges well and leads to a good accuracy of the landmark positions. |
| Databáze: | OpenAIRE |
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