nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection

• URL: http://arxiv.org/abs/2504.06742v2
• Date: Thu, 10 Apr 2025 07:04:29 GMT
• Title: nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
• Authors: Alexandra Ertl, Shuhan Xiao, Stefan Denner, Robin Peretzke, David Zimmerer, Peter Neher, Fabian Isensee, Klaus Maier-Hein,
• Abstract summary: This work introduces nnLandmark, a self-configuring deep learning framework for 3D medical landmark detection.<n>nnLandmark eliminates the need for manual parameter tuning, offering out-of-the-box usability.<n>It achieves state-of-the-art accuracy across two public datasets, with a radial mean 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)<n>nnLandmark establishes a reliable baseline for 3D landmark detection, supporting research in anatomical localization and
• Score: 35.41030755599218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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