Deep Learning-based Framework for Rapid, Precise and Fully Automated Localization of Cochlea Landmarks in 3D CT Images

Manual localization of anatomical landmarks within human cochlea structures in 3D computed tomography (CT) images is labor-intensive. However, despite progress in various studies on automatic localization methods, it remains challenging to jointly achieve rapid, precise, and fully automated landmark localization. Therefore, we propose a deep learning-based localization framework, called MEDICO-YOLO, which bridges speed and precision. It can automatically and simultaneously localize 6 human cochlea landmarks (apex, basal point, round window center of both left and right cochleae) in the full-resolution 3D CT images. The main contributions are: 1) MEDICO-YOLO learns from few manually annotated 3D CT images; 2) Within the framework, a 2D CNN-based network was developed to specifically localize cochlea landmarks in 3D single-scale medical CT images; 3) A novel loss function was designed; 4) The localization model achieved human expert-level precision while ensuring rapid inference. This study employed anonymized 3D pre-operative CT images (0.3 mm, covering the full head or temporal bone region). The evaluation involved comparing four models that were trained using 4 different training set sizes (n=4, 8, 12, 16). Each of them was evaluated on a distinct test set (n=16). The results show that the MEDICO-YOLO framework achieved strong localization performance by learning from few annotated 3D CT images. Trained on only 8 CT volumes, the model already attained a mean localization error of 1.30 mm (approximately 4 voxels). When trained on 16 CT volumes, the model achieved a mean error of 0.71 mm and a successful detection rate ([email protected] mm) of 97.92%. Furthermore, on an Apple M1 laptop, the model achieved a real-time speed (32 ms) for inferencing each 2D slice (608 x 608) and an average latency of 25.43 s for inferencing the full 3D CT image (608 x 608 x 200, including post-processing). The complete methodology for the MEDICO-YOLO framework is presented in this article.