The key insights and highlights of the content are:
Ankle fractures are difficult to diagnose due to the complex anatomy and diversity of fracture types. The authors propose an automated ankle fracture classification framework to address this challenge.
The framework consists of three main stages:
a. Tibia-fibula segmentation: A deep learning model is used to segment the tibia and fibula from CT images, including fractured regions.
b. Mask registration: The segmented masks of the injured ankle are registered to the healthy ankle masks to extract the syndesmosis region for fracture classification.
c. Semi-supervised classification: A semi-supervised learning approach is used to classify the fracture types (A, B, C) by leveraging both limited labeled data and abundant unlabeled data.
The semi-supervised classification network uses a self-training technique based on the Squeeze-and-Excitation network (SENet) to learn the non-linear distance metrics between labeled and unlabeled mask data, improving the accuracy of pseudo-labeling.
Experiments on a dataset of 612 ankle CT scans (330 labeled, 282 unlabeled) show that the proposed framework outperforms various supervised and semi-supervised methods, achieving high classification accuracy even with limited labeled data.
The tibia-fibula segmentation network also demonstrates competitive performance compared to state-of-the-art medical image segmentation methods, with high Dice scores and low Hausdorff distances.
The automated framework can simplify the complex process of ankle fracture diagnosis and classification, potentially assisting clinicians in making more accurate and efficient decisions.
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by Hongzhi Liu,... at arxiv.org 04-01-2024
https://arxiv.org/pdf/2403.19983.pdfDeeper Inquiries