Year: 2025
Type: conference-paper
Abstract: Landmark detection is essential in medical image analysis, aiding tasks like surgical navigation, diagnosis, and treatment planning. However, it remains challenging due to the need for fine-grained local detail and long-range spatial dependency modeling in high-dimensional volumetric data. Existing approaches struggle to balance accuracy, efficiency, and robustness, especially in cases of sparse landmark distribution, anatomical variability, and noisy or incomplete scans. We propose H3DE-Net, a hybrid framework combining CNNs for local feature extraction and a lightweight transformer-based attention module for global context. A volumetric bi-level routing attention mechanism reduces computational overhead while preserving long-range dependencies, and multi-scale feature fusion enhances precision and robustness. This design integrates both global and local representations, overcoming the limitations of CNN-only or transformer-only models. Extensive experiments on a public CT dataset show that H3DE-Net achieves state-of-the-art performance, significantly improving mean radial error (MRE) and success detection rate (SDR) compared to existing methods. The model is robust in challenging scenarios with missing landmarks or anatomical variations, demonstrating its applicability in real-world clinical settings. All code, pretrained weights, and data processing scripts are publicly available for reproducibility and further research.
Authors: Zhen Huang, Tao Tang, Ronghao Xu, Yangbo Wei, Wenkai Yang, Suhua Wang, Xiaoxin Sun, Han Li, Qingsong Yao
Institutions: University of Science and Technology of China, University of Electronic Science and Technology of China, Shanghai Jiao Tong University, ShanghaiTech University, Changchun Humanities and Sciences College, Northeast Normal University, Munich Center for Machine Learning, Technical University of Munich, Stanford University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7126070710