Abstract
Accurate segmentation and labeling of spine-rib are of great importance for clinical spine and rib diagnosis and treatment. In clinical applications, the spine-rib segmentation and labeling are often challenging, as the shape and appearance of vertebrae are complicated. Previous segmentation and labeling methods usually face considerable difficulties when coping with spine CT images with abnormal curvature spines and implanted metal. In this paper, we propose a multi-stage spine-rib segmentation and labeling method that can be applied to various spine-rib CT images. Our proposed method consists of three steps. First, a 3D U-Net is used to obtain a initial segmentation mask of the spine and rib. Then, the subject information, including gender, age, and the shape of the spine and rib, is used for hierarchically selecting the templates with similar physiological structures from the pre-constructed template library. Finally, the segmentation mask and label from the templates are transferred to the subject via rib-guided registration to achieve correction of the initial results. We evaluated the proposed method on a clinical dataset, and obtained significantly better and robust performance than the state-of-the-art method.
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Jiang, C. et al. (2021). Spine-Rib Segmentation and Labeling via Hierarchical Matching and Rib-Guided Registration. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_55
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