Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

Journal: Current medical imaging
PMID: 39297464

Abstract

BACKGROUND: Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention.

Authors

  • Xiaoqiang Ma
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
  • Qiang Lin
    College of Sciences, Zhejiang University of Technology, China.
  • Sihan Guo
    School of Foreign Languages, Northwest Minzu University, Lanzhou, 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.
  • Yaqiong Song
    Gansu High-Tech Innovation Service Center, 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.
  • Xiaodi Huang
    School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia.

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