A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture

TL;DR

This paper presents a deep residual U-Net model for lumen segmentation in ureteroscopy images, addressing a previously unexplored challenge with promising results for surgical assistance systems.

Contribution

It introduces a residual U-Net architecture tailored for ureteroscopy image segmentation and compares the effectiveness of gray-scale versus RGB training data.

Findings

Gray-scale training yields higher Dice, Precision, and Recall scores.

Residual U-Net shows potential for aiding surgical navigation.

Segmentation performance indicates suitability for computer-aided systems.

Abstract

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.