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

Journal: EJNMMI physics
Published Date: Jul 24, 2025

Abstract

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.

Authors

  • Yusheng Wu
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
  • Qiang Lin
    College of Sciences, Zhejiang University of Technology, China.
  • Yang He
    Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.
  • Xianwu Zeng
    Department of Nuclear Medicine, Gansu Provincial Cancer Hospital, Lanzhou, China.
  • Yongchun Cao
    School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, Gansu, China.
  • Zhengxing Man
    School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, Gansu, China.
  • Caihong Liu
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
  • Yusheng Hao
    School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China.
  • Zhengqi Cai
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
  • Jinshui Ji
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
  • Xiaodi Huang
    School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia.

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