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Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention

Ailing Xie1,2, Qiang Lin1,2,3, Yang He2,3

  • 1School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China.

Quantitative Imaging in Medicine and Surgery
|January 22, 2025
PubMed
Summary

This study introduces a deep learning model for segmenting bone metastasis (BM) on scintigraphy scans. The model improves diagnostic accuracy by automatically identifying lesions, outperforming existing methods.

Keywords:
Tumor bone metastasisbone scintigramlesion segmentationmulti-attention schememulti-scale feature learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Bone scintigraphy faces challenges in diagnosing bone metastasis (BM) due to resolution limits and lesion variability.
  • Deep learning offers automated solutions for identifying and delineating BM lesions, improving diagnostic accuracy.

Purpose of the Study:

  • To develop a deep learning-based approach for automatic segmentation of bone scintigrams.
  • To enhance the accuracy of diagnosing bone metastasis (BM) through automated lesion segmentation.

Main Methods:

  • An encoder-decoder deep learning model was developed for segmentation.
  • Multi-attention learning (Non-local Attention, Vision Transformer) and multi-scale learning strategies were employed.
  • The model was designed to enhance skeletal contrast and highlight hotspots for accurate lesion detection.

Main Results:

  • The proposed model achieved a Dice Similarity Coefficient (DSC) of 0.6720 on SPECT bone scintigrams.
  • Compared to existing models, the proposed method showed improvements of 5.6% in DSC, 2.03% in Precision, and 7.9% in Recall.

Conclusions:

  • The developed segmentation model is a promising tool for automatic extraction of metastasis lesions from SPECT bone scintigrams.
  • This approach supports the advancement of deep learning for automated characterization of bone metastasis (BM).