Multi-View Learning for Vertebrae Identification in Digitally Reconstructed Radiographs

Vertebrae localization and identification from Com-puted Tomography (CT) scans playa crucial role in the diagnosis of spine-related disorders. However, localization and labeling of vertebrae are laborious and challenging due to the complex anatomical structure of the spine, low contrast, and fuzzy bound-aries in CT scans. This study introduces an encoder-decoder-based multi-view learning approach by training the model using distinct representations (views) for vertebra identification in digitally reconstructed radiographs (DRR). Multi-view learning aims to enhance model robustness, accuracy, and generalization capabilities by leveraging information from multiple digitally acquired DRR images. To generate the DRR images, we developed a simulation environment that produces multiple DRR views from a given CT scan. We employed a contrastive learning strategy for training the backbone network to enhance the learning of global representations across these multi-views. Subsequently, we trained a localization network to detect vertebrae centroids, followed by an identification network to classify each vertebra accordingly. Moreover, we validated our model on the VerSe 2019 dataset and outperformed other state-of-the-art (SOTA) methods.