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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Flexible state space modelling for accurate and efficient 3D lung nodule detection.

Wenjia Song1, Fangfang Tang1, Henry Marshall2,3,4

  • 1School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia.

Biomedical Physics & Engineering Express
|December 9, 2025
PubMed
Summary

A new AI model, FCMamba, improves early lung cancer detection in CT scans by enhancing context awareness and reducing processing steps. This flexible connected visual state-space model offers more accurate and efficient 3D lung nodule identification for clinical use.

Keywords:
CT scanDeep learningMambaObject detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is crucial for reducing lung cancer mortality.
  • Current deep learning models like CNNs and Transformers struggle with global context, high complexity, and reliance on post-processing.
  • Existing methods often require manual tuning and post-processing steps like non-maximum suppression (NMS), hindering efficiency.

Purpose of the Study:

  • To develop a novel 3D lung nodule detection framework, FCMamba, that balances local and global contextual awareness with low computational complexity.
  • To minimize reliance on manual threshold tuning and redundant post-processing in lung nodule detection.
  • To introduce a flexible connected visual state-space model adapted from the Mamba architecture for enhanced spatial modeling.

Main Methods:

  • Proposed FCMamba, a flexible connected visual state-space model, incorporating a flexible path encoding strategy for adaptive 3D feature sequence reordering.
  • Integrated a Top Query Matcher, guided by the Hungarian matching algorithm, to replace traditional NMS for end-to-end one-to-one nodule matching.
  • Trained and evaluated the model using 10-fold cross-validation on the LIDC-IDRI dataset (888 CT scans).

Main Results:

  • FCMamba outperformed state-of-the-art CNN, Transformer, and hybrid models across various false positive per scan (FPs/scan) levels.
  • Achieved a sensitivity improvement of 2.6% to 20.3% at 0.125 FPs/scan and delivered superior CPM and FROC-AUC scores.
  • Demonstrated balanced performance across nodule sizes, reduced false positives, and improved robustness, especially in high-confidence predictions.

Conclusions:

  • FCMamba offers an efficient, scalable, and accurate solution for 3D lung nodule detection.
  • The model's flexible spatial modeling and elimination of post-processing make it suitable for clinical applications.
  • The proposed framework is adaptable for other medical imaging tasks, highlighting its versatility.