Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep
  Learning and Tracking   Real-time tool segmentation is an essential component in computer-assisted
surgical systems. We propose a novel real-time automatic method based on Fully
Convolutional Networks (FCN) and optical flow tracking. Our method exploits the
ability of deep neural networks to produce accurate segmentations of highly
deformable parts along with the high speed of optical flow. Furthermore, the
pre-trained FCN can be fine-tuned on a small amount of medical images without
the need to hand-craft features. We validated our method using existing and new
benchmark datasets, covering both ex vivo and in vivo real clinical cases where
different surgical instruments are employed. Two versions of the method are
presented, non-real-time and real-time. The former, using only deep learning,
achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming
the (non-real-time) state of the art by 3.8% points. The latter, a combination
of deep learning with optical flow tracking, yields an average balanced
accuracy of 78.2% across all the validated datasets.

22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph