Combining domain knowledge and foundation models for one-shot spine labeling

Motivation: Spine labelling is a step crucial for several important tasks such as MRI scan planning or associating image regions with mentions in clinical reports and others. Automating it can lead to significant benefits but developing automated solutions requires extensive annotations of vertebra labels. Goal(s): To automate spine labelling without extensively training a DL model with manual annotations. Approach: We adapted a vision foundational model-based approach that combines spine domain knowledge to predict spine labels. Results: Our spine labelling method gives an average accuracy of 79% and 86% for cervical and lumbar high resolution T1 images, respectively. Impact: Leveraging spatially relevant landmarks (disc) and vision foundation deep learning model, spine labels are predicted using one-shot localization. The proposed method doesn’t require any prior data for model training.