A large 4 K dataset of supermicrosurgical anastomosis at the 0.2 mm scale.

StudyScientific data· Jul 2026

A large 4 K dataset of supermicrosurgical anastomosis at the 0.2 mm scale.

Jing Liang, Jingyuan Fan, Aihemaitijiang Yusufu … Qingtang Zhu

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Microsurgery & FlapsStudyQ1IF 6.8

A new large-scale 4K video dataset was created for supermicrosurgical training and robot development.

A large 4 K dataset of supermicrosurgical anastomosis at the 0.2 mm scale.

Jing Liang … Qingtang Zhu

Scientific data · Jul 2026

Background

Supermicrosurgery, involving vessels smaller than 0.8 mm, is an important emerging field in reconstructive surgery.

40.77

Results

40.77

hours

extensive video duration in the new dataset for delicate surgery training

n = 452 procedures

More results

Its development is limited by the lack of suitable training resources and the current performance of microsurgical robotic systems.

Here, we present the Microsurgical Silicone Tube Anastomosis dataset (MSTA), a large-scale 4 K video dataset of 0.2-mm silicone tube anastomosis procedures.

More results

The participants represented a wide range of experience levels, with completion times ranging from 125 to 600 seconds, providing a broad spectrum of technical performance for analysis.

In addition, the dataset contains 47,452 segmentation annotation surgical frames, supporting computer vision studies.

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Conclusion

The MSTA dataset addresses the current gap in supermicrosurgical training data, and supports both surgical-robotics development and human training via objective performance assessment.

A large 4 K dataset of supermicrosurgical anastomosis at the 0.2 mm scale.

A large 4 K dataset of supermicrosurgical anastomosis at the 0.2 mm scale.

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