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Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation
Accurate segmentation of kidneys and kidney tumors is an essential step ...
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OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
Segmentation of endoscopic images is an essential processing step for co...
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An attempt at beating the 3D U-Net
The U-Net is arguably the most successful segmentation architecture in t...
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Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
Deep Neural Networks (DNN) have been widely used to carry out segmentati...
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Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks
This work proves that semantic segmentation on minimally invasive surgic...
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PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
Automated cervical nucleus segmentation based on deep learning can effec...
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Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation
Perioperative data are essential to investigating the causes of adverse ...
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A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
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.
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