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

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3893
• Main Authors: Chen, Shaolong, Zhang, Zhiyong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.06.2024; MDPI
• Subjects: 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
• DOI: 10.3390/s24123893

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.