The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen thoracic vertebrae and four or six lumbar vertebrae. Although the identification of enumeration anomalies has potential clinical implications for chronic back pain and operation planning, the thoracolumbar junction is often poorly assessed and rarely described in clinical reports. Additionally, even though multiple deep-learning-based vertebra labeling algorithms exist, there is a lack of methods to automatically label enumeration anomalies. Our work closes that gap by introducing "Vertebra Identification with Anomaly Handling" (VERIDAH), a novel vertebra labeling algorithm based on multiple classification heads combined with a weighted vertebra sequence prediction algorithm. We show that our approach surpasses existing models on T2w TSE sagittal (98.30% vs. 94.24% of subjects with all vertebrae correctly labeled, p < 0.001) and CT imaging (99.18% vs. 77.26% of subjects with all vertebrae correctly labeled, p < 0.001) and works in arbitrary field-of-view images. VERIDAH correctly labeled the presence 2 Möller et al. of thoracic enumeration anomalies in 87.80% and 96.30% of T2w and CT images, respectively, and lumbar enumeration anomalies in 94.48% and 97.22% for T2w and CT, respectively. Our code and models are available at: https://github.com/Hendrik-code/spineps.

翻译：人类脊柱通常由七节颈椎、十二节胸椎和五节腰椎组成。然而，计数异常可能导致个体拥有十一或十三节胸椎以及四或六节腰椎。尽管识别计数异常对慢性背痛和手术规划具有潜在的临床意义，但胸腰连接处常在临床报告中评估不足且鲜有描述。此外，尽管存在多种基于深度学习的椎体标注算法，但尚缺乏能够自动标注计数异常的方法。我们的工作通过引入"具有异常处理能力的椎体识别"（VERIDAH）填补了这一空白，这是一种基于多分类头结合加权椎体序列预测算法的新型椎体标注算法。我们证明，在T2加权TSE矢状位成像（所有椎体正确标注的受试者比例：98.30% vs. 94.24%，p < 0.001）和CT成像（所有椎体正确标注的受试者比例：99.18% vs. 77.26%，p < 0.001）上，我们的方法超越了现有模型，并能在任意视野图像中工作。VERIDAH在T2加权和CT图像中分别以87.80%和96.30%的正确率标注了胸椎计数异常的存在，并以94.48%和97.22%的正确率分别标注了腰椎计数异常。我们的代码和模型可在以下网址获取：https://github.com/Hendrik-code/spineps。