  During laparoscopic surgery, context-aware assistance systems aim to
alleviate some of the difficulties the surgeon faces. To ensure that the right
information is provided at the right time, the current phase of the
intervention has to be known. Real-time locating and classification the
surgical tools currently in use are key components of both an activity-based
phase recognition and assistance generation.
  In this paper, we present an image-based approach that detects and classifies
tools during laparoscopic interventions in real-time. First, potential
instrument bounding boxes are detected using a pixel-wise random forest
segmentation. Each of these bounding boxes is then classified using a cascade
of random forest. For this, multiple features, such as histograms over hue and
saturation, gradients and SURF feature, are extracted from each detected
bounding box.
  We evaluated our approach on five different videos from two different types
of procedures. We distinguished between the four most common classes of
instruments (LigaSure, atraumatic grasper, aspirator, clip applier) and
background. Our method succesfully located up to 86% of all instruments
respectively. On manually provided bounding boxes, we achieve a instrument type
recognition rate of up to 58% and on automatically detected bounding boxes up
to 49%.
  To our knowledge, this is the first approach that allows an image-based
classification of surgical tools in a laparoscopic setting in real-time.
