VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences

TL;DR

VERIDAH addresses the challenge of labeling vertebrae in CT and T2w MRI when enumeration anomalies (TEA/LEA) are present and when field-of-view is arbitrary. It combines a multi-head classifier with a constrained sequence predictor based on dynamic programming to produce consistent vertebral sequences that respect anatomical and anomaly-specific constraints. Across CT and MRI, VERIDAH outperforms prior methods with high perfect-label rates and strong anomaly recalls, while showing robustness to varying FOVs. The approach offers clinically relevant, anomaly-aware labeling and is publicly released to enable broader adoption and evaluation.

Abstract

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.

Figures (9)

• Figure 1: Example data samples from our CT (A, B) and MRI (C, D) dataset. For each sample, it shows a sagittal and coronal projection along the spine as well as the corpus centroids with vertebra labels associated with each. While the CT images contain various field-of-views, the NAKO T2w sagittal MRI images always cover the whole spine. A) spine with eleven thoracic vertebrae, B) normal spine with twelve thoracic and five lumbar vertebrae, C) has an additional thoracic vertebra, and D), a subject with only eleven thoracic vertebrae but six lumbar ones.
• Figure 2: Overview of the VERIDAH approach. We take an input image and the vertebra localizations to crop around each vertebra. We then input these 3D cropped images separately into our classification model. The classification model has four output heads. We weigh their predictions based on our calibration and combine them into a cost matrix. Finally, an algorithm finds the optimal label sequence path that minimizes this cost, making our final sequence prediction.
• Figure 3: Example of our sequence predictor on an artificially created sample containing $n=4$ vertebrae ($V_1$ -- $V_4$) in the FOV. The classifier outputs specific vertebra class predictions $c_{ij}$ and region prediction scores $r_{ij}$ for each class (already combined into the label cost matrix $L$ and reduced to T11 to L2 for simplicity), their visibility $s_i$, and the vertebra transitions output $t_{ik}$. Depending on the utilized classification head, either the path [T11, T12, L1, L2] (red arrows) or [T11, T12, T12, L1] (blue arrows) is returned by the sequence predictor. In the case of the latter one, the post-processing relabels the path [T11, T12, T12, L1] to [T11, T12, T13, L1], making this a TEA case. All unspecified classifier outputs have a value of zero.
• Figure 4: Example of a CT test subject where all models failed. The reference data (left) shows an L6 LEA case with an ordinary number of twelve thoracic vertebrae. VERIDAH (right) mistakes the L1 for a thoracic vertebra, resulting in thirteen thoracic and five lumbar vertebrae (red arrows). The prediction of Meng et al. has a shift error by one, but correctly labels the L1. The center shows the reference T12 and L1 in multiple axial slices (top to bottom). While the T12 has clear ribs on both sides (yellow arrows), the L1 has thoracic-looking facets while having no ribs, making this a TLTV.
• Figure 5: Example of a CT test subject with a T13 and only four lumbar vertebrae. The axial slices of the reference data (left) show that the T13 has a rib on either side (yellow arrows). While VERIDAH (right) correctly classifies the whole sequence of labels, the prediction of Meng et al. made two major errors, leading to six lumbar vertebrae instead of four (red arrows).