Accurate localization and identification of vertebrae in spinal imaging is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. The main difficulties for automatic methods arise from the frequent presence of abnormal spine curvature, small field of view, and image artifacts caused by surgical implants. Many previous methods rely on parametric models of appearance and shape whose performance can substantially degrade for pathological cases.
We propose a robust localization and identification algorithm which builds upon supervised classification forests and avoids an explicit parametric model of appearance. We overcome the tedious requirement for dense annotations by a semi-automatic labeling strategy. Sparse centroid annotations are transformed into dense probabilistic labels which capture the inherent identification uncertainty. Using the dense labels, we learn a discriminative centroid classifier based on local and contextual intensity features which is robust to typical characteristics of spinal pathologies and image artifacts. Extensive evaluation is performed on a challenging dataset of 224 spine CT scans of patients with varying pathologies including high-grade scoliosis, kyphosis, and presence of surgical implants. Additionally, we test our method on a heterogeneous dataset of another 200, mostly abdominal, CTs. Quantitative evaluation is carried out with respect to localization errors and identification rates, and compared to a recently proposed method. Our approach is efficient and outperforms state-of-the-art on pathological cases.