论文标题
TRASETR：带有对比度查询的轨道对段变压器，用于机器人手术中的实例级仪器分割
TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery
论文作者
论文摘要
手术仪器分割 - 一般而言，像素分类任务 - 从根本上对于促进机器人辅助手术（RAS）的认知智能至关重要。但是，以前的方法正在努力区分仪器类型和实例。为了解决上述问题，我们探索了产生每段预测的面具分类范式。我们提出了Trasetr，这是一种新型的轨道到段变压器，明智地利用跟踪提示来帮助手术仪器分割。 trasetr联合出于仪器类型，位置和身份的原因，并通过解码查询嵌入方式来实例级预测，即一组Bbox掩码对。具体而言，我们介绍了先前的时间知识编码的先前查询，以通过身份匹配将跟踪信号传输到当前实例。进一步采用了对比的查询学习策略来重塑查询功能空间，这极大地减轻了由于时间变化较大而引起的跟踪难度。在三个公共数据集上使用最新的仪器类型分割结果证明了我们方法的有效性，其中包括来自Endovis挑战的两个RAS基准和一个白内障手术数据集Cadis。
Surgical instrument segmentation -- in general a pixel classification task -- is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address the above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-level predictions i.e., a set of class-bbox-mask pairs, by decoding query embeddings. Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching. A contrastive query learning strategy is further applied to reshape the query feature space, which greatly alleviates the tracking difficulty caused by large temporal variations. The effectiveness of our method is demonstrated with state-of-the-art instrument type segmentation results on three public datasets, including two RAS benchmarks from EndoVis Challenges and one cataract surgery dataset CaDIS.
