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

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-02 DOI:10.1038/s41597-025-05897-7

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

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Abstract

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.

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脊柱内窥镜图谱：手术器械分割的开源数据集。

内窥镜脊柱手术（ESS）是一种微创手术，用于脊神经减压、椎间盘突出切除和脊柱融合术。尽管它有许多优点，但其陡峭的学习曲线对广泛采用构成了挑战。人工智能（AI）系统的发展对于提高ESS的精度和安全性至关重要。手术器械的自动分割是实现手术辅助系统智能化的关键一步。因此，本文创建了脊柱内窥镜图集（SEA）数据集，这是一个全面的带注释的图像集合，包括脊柱内窥镜手术中常用的所有器械。SEA总共包含48,510张图像和10,662张来自真实世界ESS的仪器分割。该数据集专门用于训练用于精确仪器分割的深度学习模型。通过对五个模型的验证，我们展示了数据集在提高复杂条件下的分割精度方面的价值，为未来人工智能在ESS中的发展奠定了基础。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.