Localization of ear landmarks in head CT images using self-supervised anatomical embeddings

Localization of ear landmarks in full head computed tomographic (CT) images can facilitate automated analysis of the inner ear anatomy by identifying and aligning the regions-of-interest (ROIs) around the ear. This study explores unsupervised methods for localizing ear landmarks using the Self-Supervised Anatomical Embeddings (SAME) framework, which leverages contrastive learning to learn voxel-wise anatomical representations for 3D volumes. We define seven landmarks in an atlas image and localize these landmarks in testing images by comparing the embeddings of the atlas landmarks to the embeddings of each voxel in the testing images. Our experiments show that this unsupervised method achieves results comparable to a convolutional neural network trained to predict the same landmarks in a supervised manner. Importantly, the unsupervised approach does not require ground-truth labels for training and can be applied to localize other landmarks without retraining. Additionally, we explore metrics to assess the reliability of landmarks predicted using the unsupervised method. We find that the number and distribution of voxels in the test image with high embedding similarity to a given landmark in the atlas serves as a good indicator of the expected localization error. This insight allows for the selection of points that can be more reliably localized without the need for direct measurement of localization errors, which would require ground-truth labels. This study demonstrates the potential of unsupervised learning techniques for anatomical landmark localization in head CT images, offering a scalable solution that eliminates the need for extensive manual annotation.