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<p>Endoscopic spine surgery (ESS) is a minimally invasive procedure used for spinal nerve decompression, herniated disc removal, and spinal fusion. Despite its many advantages, its steep learning curve poses a challenge to widespread adoption. The development of artificial intelligence (AI) systems is crucial for enhancing the precision and safety of ESS. The automatic segmentation of surgical instruments is a key step towards realizing intelligent surgical assistance systems. As such, this paper has created the Spine Endoscopic Atlas (SEA) dataset, a comprehensive collection of annotated images encompassing all instruments commonly used in spinal endoscopic surgery. In total, SEA contains 48,510 images and 10,662 instrument segmentations derived from real-world ESS. This dataset is specifically designed to train deep learning models for precise instrument segmentation. Through validation of five models, we demonstrate the dataset’s value in improving segmentation accuracy under complex conditions, providing a foundation for future AI advancements in ESS.</p>

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Spine endoscopic atlas: an open-source dataset for surgical instrument segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月2日

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  • Zhipeng Xu,
  • Hong Wang,
  • Yongxian Huang,
  • Jianjin Zhang,
  • Yanhong Chen,
  • Shangjie Wu,
  • Zhouyang Hu,
  • Guanghui Yue,
  • Jax Luo,
  • Guoxin Fan,
  • Xiang Liao

摘要

Endoscopic spine surgery (ESS) is a minimally invasive procedure used for spinal nerve decompression, herniated disc removal, and spinal fusion. Despite its many advantages, its steep learning curve poses a challenge to widespread adoption. The development of artificial intelligence (AI) systems is crucial for enhancing the precision and safety of ESS. The automatic segmentation of surgical instruments is a key step towards realizing intelligent surgical assistance systems. As such, this paper has created the Spine Endoscopic Atlas (SEA) dataset, a comprehensive collection of annotated images encompassing all instruments commonly used in spinal endoscopic surgery. In total, SEA contains 48,510 images and 10,662 instrument segmentations derived from real-world ESS. This dataset is specifically designed to train deep learning models for precise instrument segmentation. Through validation of five models, we demonstrate the dataset’s value in improving segmentation accuracy under complex conditions, providing a foundation for future AI advancements in ESS.