TEDi: Temporal Memory-Enhanced and Denoising Transformer for Surgical Instrument Segmentation

2026-09-15

AI Summary

TEDi通过引入记忆搜索增强和时间一致性降噪解决手术器械分割中跨帧信息利用不足的问题，提高分割稳定性与准确性。

Abstract

Query-based segmentation methods have shown promising potential for surgical instrument segmentation and recognition, which is essential for scene understanding and downstream tasks in computer assisted surgery. However, most existing approaches predominantly rely on per-frame predictions and overlook cross-frame temporal priors as well as temporal-consistency constraints. This limitation often leads to unstable query representations and suboptimal category recognition. In this paper, we propose TEDi, a Temporal memory-Enhanced and Denoising transformer for surgical instrument segmentation that addresses these is sues through Memory Search Enhancement and Temporal Consistency Denoising. The former introduces a query-level memory bank and a memory search enhancement encoder to retrieve discriminative representations from historical frames, enriching current-frame features. The latter constructs a temporally consistent reference as a cross-frame semantic anchor to suppress temporally unstable predictions and promote semantic coherence across frames. Extensive experiments on two benchmark datasets, EndoVis 2017 and EndoVis 2018, demonstrate that TEDi consistently outperforms state-of-the-art methods, highlighting its potential to further advance computer-assisted surgery. Our code is available at github.com/argon-xixi/TEDi.

Problem

• surgical instrument segmentation
• temporal priors
• temporal consistency

Innovation

• Temporal Memory-Enhanced
• Denoising Transformer
• Memory Search Enhancement
• Temporal Consistency Denoising