Title: Deep Residual Learning for Instrument Segmentation in Robotic Surgery,
Abstract: Detection, tracking, and pose estimation of surgical instruments are crucial
tasks for computer assistance during minimally invasive robotic surgery. In the
majority of cases, the first step is the automatic segmentation of surgical
tools. Prior work has focused on binary segmentation, where the objective is to
label every pixel in an image as tool or background. We improve upon previous
work in two major ways. First, we leverage recent techniques such as deep
residual learning and dilated convolutions to advance binary-segmentation
performance. Second, we extend the approach to multi-class segmentation, which
lets us segment different parts of the tool, in addition to background. We
demonstrate the performance of this method on the MICCAI Endoscopic Vision
Challenge Robotic Instruments dataset.