A challenging aspect of instrument segmentation in robotic surgery is to distinguish different parts of the same instrument. Parts with similar textures are common in a practical instrument and are difficult to distinguish. In this work, the authors introduce an end-to-end recurrent model that comprises a multiscale semantic segmentation network and a refinement model. Specifically, the semantic segmentation network uniformly transforms the input images in multiple scales into a semantic mask, and the refinement model is a single-scale net recurrently optimising the above semantic mask. Through extensive experiments, the authors validate that the models with multiscale inputs perform better than those to fuse encoded feature maps and ones with spatial attention. Furthermore, the authors verify the effectiveness of the proposed model with state-of-the-art performances on several robotic instrument datasets derived from MICCAI Endoscopic Vision Challenges.

中文翻译：

---

机器人手术器械零件分割的多尺度问题

机器人手术中器械分割的一个挑战性方面是区分同一器械的不同部分。具有相似纹理的零件在实际仪器中很常见，很难区分。在这项工作中，作者介绍了一个端到端循环模型，该模型包括一个多尺度语义分段网络和一个精炼模型。具体地，语义分割网络将输入的多个尺度的图像均匀地转换为语义掩码，细化模型为递归优化上述语义掩码的单尺度网络。通过广泛的实验，作者验证了具有多尺度输入的模型比融合编码的特征图和具有空间注意力的模型的性能更好。此外，