A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic ann...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 24; H. 12; S. 3893
Hauptverfasser: Chen, Shaolong, Zhang, Zhiyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.06.2024
MDPI
Schlagworte:
active learning
Algorithms
Annotations
Data mining
Datasets
Deep learning
Efficiency
Machine learning
magnetic resonance image
Magnetic resonance imaging
Medical imaging equipment
Medical research
semi-automatic annotation
semi-supervised learning
weakly supervised learning
magnetic resonance image
semi-automatic annotation
active learning
semi-supervised learning
weakly supervised learning
ISSN:1424-8220, 1424-8220
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Zusammenfassung:The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24123893