3 research outputs found
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular, we reformulate the 2D instrument pose estimation as heatmap
regression and thereby enable a concurrent, robust and near real-time
regression of both tasks via deep learning. As demonstrated by our experimental
results, this modeling leads to a significantly improved performance than
directly regressing the tool position and allows our method to outperform the
state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
Challenge 2015.Comment: I. Laina and N. Rieke contributed equally to this work. Accepted to
MICCAI 201
Simultaneous recognition and pose estimation of instruments in minimally invasive surgery
Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences