nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
This addresses the need for automated, reproducible landmark detection in medical imaging for diagnosis and treatment planning, though it appears incremental as it builds on nnU-Net.
The paper tackles the problem of automating 3D medical landmark detection by introducing nnLandmark, a self-configuring deep learning framework that adapts nnU-Net for heatmap-based regression, achieving state-of-the-art accuracy with mean radial errors of 1.5 mm on a dental CT dataset and 1.2 mm on a brain MRI dataset.
Landmark detection plays a crucial role in medical imaging tasks that rely on precise spatial localization, including specific applications in diagnosis, treatment planning, image registration, and surgical navigation. However, manual annotation is labor-intensive and requires expert knowledge. While deep learning shows promise in automating this task, progress is hindered by limited public datasets, inconsistent benchmarks, and non-standardized baselines, restricting reproducibility, fair comparisons, and model generalizability. This work introduces nnLandmark, a self-configuring deep learning framework for 3D medical landmark detection, adapting nnU-Net to perform heatmap-based regression. By leveraging nnU-Net's automated configuration, nnLandmark eliminates the need for manual parameter tuning, offering out-of-the-box usability. It achieves state-of-the-art accuracy across two public datasets, with a mean radial error (MRE) of 1.5 mm on the Mandibular Molar Landmark (MML) dental CT dataset and 1.2 mm for anatomical fiducials on a brain MRI dataset (AFIDs), where nnLandmark aligns with the inter-rater variability of 1.5 mm. With its strong generalization, reproducibility, and ease of deployment, nnLandmark establishes a reliable baseline for 3D landmark detection, supporting research in anatomical localization and clinical workflows that depend on precise landmark identification. The code will be available soon.