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<p>Anatomical landmark detection is crucial in medical image analysis, facilitating accurate diagnosis, surgical planning, and treatment evaluation. However, existing methods often struggle to simultaneously capture global context and local details while exhibiting limited generalization across diverse datasets and imaging modalities. To relieve this, we propose a hybrid model that leverages convolutional operations to capture local information and a Swin Transformer to enhance global context. Specifically, we introduce a novel U-shaped architecture, termed Convolutional Attention Swin Enhanced Landmark Detection Network (CASEMark). CASEMark integrates three key innovations: (1) a Convolutional Attention Swin Transformer module (CAST) that integrates transformer-based global context modeling with convolutional operations for local feature extraction, (2) an Enhanced Skip Attention Module (ESAM) enabling adaptive feature fusion between encoder and decoder pathways, and (3) a multi-resolution heatmap learning strategy that aggregates information across scales. This approach effectively balances global-local feature extraction with robust cross-modality generalization. Extensive experiments on four public datasets demonstrate the superiority of CASEMark. The code and datasets will be made publicly available.</p>

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CASEMark: A hybrid model for robust anatomical landmark detection in multi-structure X-rays

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月12日

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  • Zhen Huang,
  • Xiaoqian Zhou,
  • Xu He,
  • Yangbo Wei,
  • Wenkai Yang,
  • Suhua Wang,
  • Xiaoxin Sun,
  • Han Li

摘要

Anatomical landmark detection is crucial in medical image analysis, facilitating accurate diagnosis, surgical planning, and treatment evaluation. However, existing methods often struggle to simultaneously capture global context and local details while exhibiting limited generalization across diverse datasets and imaging modalities. To relieve this, we propose a hybrid model that leverages convolutional operations to capture local information and a Swin Transformer to enhance global context. Specifically, we introduce a novel U-shaped architecture, termed Convolutional Attention Swin Enhanced Landmark Detection Network (CASEMark). CASEMark integrates three key innovations: (1) a Convolutional Attention Swin Transformer module (CAST) that integrates transformer-based global context modeling with convolutional operations for local feature extraction, (2) an Enhanced Skip Attention Module (ESAM) enabling adaptive feature fusion between encoder and decoder pathways, and (3) a multi-resolution heatmap learning strategy that aggregates information across scales. This approach effectively balances global-local feature extraction with robust cross-modality generalization. Extensive experiments on four public datasets demonstrate the superiority of CASEMark. The code and datasets will be made publicly available.