VERIDAH: solving enumeration anomaly-aware vertebra labeling across imaging sequences.
Authors
Affiliations (13)
Affiliations (13)
- Department of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Munich, Germany. [email protected].
- Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany. [email protected].
- Department of Diagnostic and Interventional Neuroradiology, TUM University Hospital, Munich, Germany.
- Department of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany.
- Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal and Mila (Quebec AI Institute), Quebec, Canada.
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Freiburg, Germany.
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine, Berlin, Germany.
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany.
- Department of Computing, Imperial College London, London, UK.
Abstract
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. 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. 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. VERIDAH offers enumeration anomaly robust vertebra labeling in CT and T2w sagittal imaging and is publicly released along with a spine segmentation model. QuestionAutomatically identifying enumeration anomalies in the spine is important in determining the correct vertebra labels, especially in limited field-of-view imaging. FindingsA carefully designed classifier with multiple classification heads and a sequence predictor provides superior results on large benchmarks, both in CT and MRI. Clinical relevanceThe proposed method identifies vertebra levels even in very limited field-of-view imaging, and correctly predicts enumeration anomalies, making downstream tasks such as surgery planning easier. Its public availability enables studies and applications to take these spinal anomalies into account.