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GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation.

Ruijie Ming1, Fengpin Wang2, Taotao Zheng1

  • 1Department of Oncology, Chongqing University Three Gorges Hospital, School of Medicine, Chongqing University, Chongqing, China.

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Accurate pulmonary nodule detection in CT scans is improved by GLANCE, a novel dual-stream deep learning model. This approach effectively combines global context and local details for enhanced early lung cancer diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate segmentation and detection of pulmonary nodules in computed tomography (CT) scans are crucial for early lung cancer diagnosis.
  • Existing deep learning models face challenges due to nodule diversity and limitations in capturing both long-range context and fine local details.

Purpose of the Study:

  • To introduce GLANCE (Continuous Global-Local Exchange with Consensus Fusion), a novel dual-stream deep learning architecture designed to overcome limitations in pulmonary nodule detection.
  • To improve the accuracy of simultaneous nodule segmentation and center heatmap detection in CT scans.

Main Methods:

  • Developed GLANCE, a dual-stream architecture with parallel branches: a global context transformer for long-range dependencies and a multi-receptive grouped atrous mixer for local details.
  • Implemented a cross-scale consensus fusion mechanism to continuously integrate complementary feature streams at all hierarchical scales.
  • Utilized a dual-head pyramid refinement decoder for simultaneous segmentation and detection tasks.

Main Results:

  • GLANCE achieved state-of-the-art performance in both pulmonary nodule segmentation and detection across four public benchmarks (LIDC-IDRI, LNDb, LUNA16, and Tianchi).
  • An ablation study validated the critical contribution of each architectural component, especially the continuous fusion strategy, to the model's superior performance.

Conclusions:

  • The proposed GLANCE architecture effectively addresses the limitations of existing models by synergistically combining global and local feature learning.
  • GLANCE represents a significant advancement in automated pulmonary nodule analysis, offering improved accuracy for early lung cancer diagnosis.