VERIDAH: solving enumeration anomaly-aware vertebra labeling across imaging sequences

OBJECTIVES: Although the identification of enumeration anomalies has potential clinical implications for chronic back pain and operation planning, the thoracolumbar junction is 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. MATERIALS AND METHODS: We introduce “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 use the NAKO dataset for MRI and a CT cohort to retrospectively evaluate the labeling performance by computing how often the model is correct in predicting the vertebra labels, including enumeration anomalies. Statistical significance was assessed using the Wilcoxon signed-rank test. RESULTS: On 1000 T2w sagittal (mean age 52 ±11, 52% female) and 365 CT (mean age 74±21, 44% female) images, 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 < 0.01) and CT imaging (99.18% vs. 77.26% of subjects with all vertebrae correctly labeled, p < 0.01) and works in arbitrary field-of-view images. VERIDAH correctly labeled the presence 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. CONCLUSION: VERIDAH offers enumeration anomaly robust vertebra labeling in CT and T2w sagittal imaging and is publicly released along with a spine segmentation model.