Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae, taking advantage of advancements in automated multi-label segmentation and the availability of large datasets for unsupervised learning. Our shape auto-encoders are trained on a large set of vertebrae surface patches, leveraging the vast amount of available data for vertebra segmentation. This addresses the label scarcity problem faced when learning shape information of vertebrae from image intensities. Based on the learned shape features we train an MLP to detect vertebral body fractures. Using segmentation masks that were automatically generated using the TotalSegmentator, our proposed method achieves an AUC of 0.901 on the VerSe19 testset. This outperforms image-based and surface-based end-to-end trained models. Additionally, our results demonstrate that pre-training the models in an unsupervised manner enhances geometric methods like PointNet and DGCNN. Our findings emphasise the advantages of explicitly learning shape features for diagnosing osteoporotic vertebrae fractures. This approach improves the reliability of classification results and reduces the need for annotated labels. This study provides novel insights into the effectiveness of various encoder-decoder models for shape analysis of vertebrae and proposes a new decoder architecture: the point-based shape decoder.

翻译：老年人群脊柱退行性病变发病率较高。及时诊断骨质疏松性骨折及其他退行性畸形，有助于采取主动措施降低严重背痛和残疾风险。本研究利用自动化多标签分割技术的进展以及大规模数据集的可用性，专门探索了用于椎骨的形状自编码器。我们的形状自编码器通过大量椎骨表面斑块进行训练，充分利用了丰富的椎骨分割数据，从而解决了从图像强度学习椎骨形状信息时面临的标签稀缺问题。基于学习到的形状特征，我们训练了一个多层感知机来检测椎体骨折。通过使用TotalSegmentator自动生成的分割掩膜，所提方法在VerSe19测试集上达到了0.901的AUC值，优于基于图像和基于表面的端到端训练模型。此外，研究结果表明，以无监督方式预训练模型能够增强PointNet和DGCNN等几何方法的性能。我们的发现强调了显式学习形状特征对于诊断骨质疏松性椎骨骨折的优势，该方法提高了分类结果的可靠性，并减少了对标注标签的需求。本研究为不同编码器-解码器模型在椎骨形状分析中的有效性提供了新见解，并提出了一种新型解码器架构：基于点状形状解码器。