In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich
using endoscope images of ex-vivo tissue with automatically generated
annotations from robot forward kinematics and instrument CAD models. However,
the limited background variation and simple motion rendered the dataset
uninformative in learning about which techniques would be suitable for
segmentation in real surgery. In 2017, at the same workshop in Quebec we
introduced the robotic instrument segmentation dataset with 10 teams
participating in the challenge to perform binary, articulating parts and type
segmentation of da Vinci instruments. This challenge included realistic
instrument motion and more complex porcine tissue as background and was widely
addressed with modifications on U-Nets and other popular CNN architectures. In
2018 we added to the complexity by introducing a set of anatomical objects and
medical devices to the segmented classes. To avoid over-complicating the
challenge, we continued with porcine data which is dramatically simpler than
human tissue due to the lack of fatty tissue occluding many organs.

通过 EndoVis 工作坊提出的机器人仪器分割数据集，在模拟真实手术时应用于学习适用的技术，其中包括了最先进的 U-Net 和其他流行的 CNN 架构，并且逐步增加这个数据集的复杂性，包括针对器械，类型和解剖器官的分割。