TEDi：用于手术器械分割的时间记忆增强与去噪Transformer

中文摘要 AI 辅助

基于查询的分割方法在手术器械分割与识别中展现出巨大潜力，这对于计算机辅助手术中的场景理解和下游任务至关重要。然而，大多数现有方法主要依赖逐帧预测，忽视了跨帧时间先验以及时间一致性约束。这一局限性常常导致查询表示不稳定和类别识别次优。在本文中，我们提出TEDi，一种用于手术器械分割的时间记忆增强与去噪Transformer，通过记忆搜索增强和时间一致性去噪来解决这些问题。前者引入查询级记忆库和记忆搜索增强编码器，从历史帧中检索判别性表示，以丰富当前帧特征。后者构建时间一致的参考作为跨帧语义锚点，以抑制时间不稳定的预测并促进跨帧语义一致性。在两个基准数据集EndoVis 2017和EndoVis 2018上的大量实验表明，TEDi始终优于最先进的方法，凸显了其进一步推进计算机辅助手术的潜力。我们的代码可在以下网址获取：此http URL。

英文摘要

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.

发表机构

• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。