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H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging by Huang, Zhen; Xu, Ronghao; Zhou, Xiaoqian; Wei, Yangbo; Wang, Suhua; Sun, Xiaoxin; Li, Han; Yao, Qingsong is a scholarly article available to read on EtoBox.

What is H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging about?

3D landmark detection is a critical task in medical image analysis, and accurately detecting anatomical landmarks is essential for subsequent medical imaging tasks. However, mainstream deep learning methods in this field struggle to simultaneously capture fine-grained local features and model global spatial relationships, while maintaining a balance between accuracy and computational efficiency. Local feature extraction requires capturing fine-grained anatomical details, while global modeling requires understanding the spatial relationships within complex anatomical structures. The high-dimensional nature of 3D volume further exacerbates these challenges, as landmarks are sparsely distributed, leading to significant computational costs. Therefore, achieving efficient and precise 3D landmark detection remains a pressing challenge in medical image analysis. In this work, We propose a \textbf{H}ybrid \textbf{3}D \textbf{DE}tection \textbf{Net}(H3DE-Net), a novel framework that combines CNNs for local feature extraction with a lightweight attention mechanism designed to efficiently capture global dependencies in 3D volumetric data. This mechanism employs a hierarchical routing strategy

Author

Huang, Zhen; Xu, Ronghao; Zhou, Xiaoqian; Wei, Yangbo; Wang, Suhua; Sun, Xiaoxin; Li, Han; Yao, Qingsong

Published

2025

Language

EN