Instance segmentation of surgical instruments is a long-standing research
problem, crucial for the development of many applications for computer-assisted
surgery. This problem is commonly tackled via fully-supervised training of deep
learning models, requiring expensive pixel-level annotations to train. In this
work, we develop a framework for instance segmentation not relying on spatial
annotations for training. Instead, our solution only requires binary tool
masks, obtainable using recent unsupervised approaches, and binary tool
presence labels, freely obtainable in robot-assisted surgery. Based on the
binary mask information, our solution learns to extract individual tool
instances from single frames, and to encode each instance into a compact vector
representation, capturing its semantic features. Such representations guide the
automatic selection of a tiny number of instances (8 only in our experiments),
displayed to a human operator for tool-type labelling. The gathered information
is finally used to match each training instance with a binary tool presence
label, providing an effective supervision signal to train a tool instance
classifier. We validate our framework on the EndoVis 2017 and 2018 segmentation
datasets. We provide results using binary masks obtained either by manual
annotation or as predictions of an unsupervised binary segmentation model. The
latter solution yields an instance segmentation approach completely free from
spatial annotations, outperforming several state-of-the-art fully-supervised
segmentation approaches.

通过仅使用二进制工具遮罩以及机器人辅助手术中的二进制工具存在标签，我们开发了一个无需空间注释的实例分割框架，该框架能从单帧中提取个体工具实例并进行语义特征编码，使得只需以人类操作员进行工具类别标记的方式便能有效训练工具实例分类器。通过验证我们的框架在 EndoVis 2017 和 2018 分割数据集上，我们发现与几种最先进的全监督分割方法相比，通过手动注释或基于无监督二进制分割模型的预测获得的二进制遮罩的实例分割方法无需空间注释且性能更优。