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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Published on: August 23, 2017

Surgical Instrument Segmentation via Segment-Then-Classify Framework with Instance-Level Spatiotemporal Consistency

Tiyao Zhang¹, Xue Yuan¹, Hongze Xu¹

¹School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China.

Journal of Imaging

|October 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel Segment-then-Classify framework for precise surgical instrument segmentation in endoscopic videos, improving robot-assisted surgery accuracy and stability.

Keywords:

instance-level spatiotemporal consistency modeling segment-then-classify framework surgical instrument segmentation

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Area of Science:

Robotics
Computer Vision
Medical Imaging

Background:

Accurate segmentation of surgical instruments is vital for robot-assisted surgery and intraoperative analysis.
Existing methods struggle with spatial completeness and temporal stability, especially under occlusion or motion blur.

Purpose of the Study:

To present a Segment-then-Classify framework that decouples mask generation from semantic classification.
To enhance spatial completeness and temporal stability in surgical instrument segmentation.
To improve interpretability and robustness in challenging surgical video conditions.

Main Methods:

Utilized a Mask2Former-based segmentation backbone for class-agnostic instance mask and region feature generation.
Employed a bounding box-guided instance-level spatiotemporal modeling module.
Fused geometric priors and temporal consistency using a lightweight transformer encoder.

Main Results:

Achieved significant improvements in mean Intersection over Union (mIoU) by 3.06%, 2.99%, and 1.67% on EndoVis datasets.
Demonstrated substantial gains in mean correspondence Intersection over Union (mcIoU) of 2.36%, 2.85%, and 6.06% over state-of-the-art methods.
Maintained computational efficiency while enhancing segmentation performance.

Conclusions:

The proposed Segment-then-Classify framework effectively enhances spatial completeness and temporal stability in surgical instrument segmentation.
The framework shows superior performance and robustness compared to existing methods on benchmark datasets.
This approach offers a promising solution for improving accuracy and reliability in robot-assisted surgery.