  Ureteroscopy is becoming the first surgical treatment option for the majority
of urinary affections. This procedure is performed using an endoscope which
provides the surgeon with the visual information necessary to navigate inside
the urinary tract. Having in mind the development of surgical assistance
systems, that could enhance the performance of surgeon, the task of lumen
segmentation is a fundamental part since this is the visual reference which
marks the path that the endoscope should follow. This is something that has not
been analyzed in ureteroscopy data before. However, this task presents several
challenges given the image quality and the conditions itself of ureteroscopy
procedures. In this paper, we study the implementation of a Deep Neural Network
which exploits the advantage of residual units in an architecture based on
U-Net. For the training of these networks, we analyze the use of two different
color spaces: gray-scale and RGB data images. We found that training on
gray-scale images gives the best results obtaining mean values of Dice Score,
Precision, and Recall of 0.73, 0.58, and 0.92 respectively. The results
obtained shows that the use of residual U-Net could be a suitable model for
further development for a computer-aided system for navigation and guidance
through the urinary system.
