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Background <p>Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes.</p> Methods <p>We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning.</p> Results <p>The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 — outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions.</p> Conclusion <p>Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.</p>

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MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月24日

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  • Yusheng Wu,
  • Qiang Lin,
  • Yang He,
  • XianWu Zeng,
  • Yongchun Cao,
  • ZhengXing Man,
  • Caihong Liu,
  • Yusheng Hao,
  • Zhengqi Cai,
  • Jinshui Ji,
  • Xiaodi Huang

摘要

Background

Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bone metastases from lung cancer. However, its low spatial resolution and lesion similarity to benign structures present significant challenges for accurate segmentation, especially for lesions of varying sizes.

Methods

We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. The generator employs cascade dilated convolutions, multi-scale modules, and deep supervision, while the discriminator utilizes multi-scale L1 loss computed on image-mask pairs to guide segmentation learning.

Results

The proposed model was evaluated on a dataset of 286 clinically annotated SPECT scintigrams. It achieved a Dice Similarity Coefficient (DSC) of 0.6671, precision of 0.7228, and recall of 0.6196 — outperforming both classical and recent adversarial segmentation models in multi-scale lesion detection, especially for small and clustered lesions.

Conclusion

Our results demonstrate that the integration of multi-scale feature learning with adversarial supervision significantly improves the segmentation of bone metastasis in SPECT imaging. This approach shows potential for clinical decision support in the management of lung cancer.