Endoscope-based surgery has several beneficial effects regarding the rehabilitation
of the patients, but has some drawbacks causing difficulties to medical experts, on
the contrary. The main disadvantage is that the tactile information is lost to the
expert who takes the surgical intervention. There are some organs (e.g. ureters and
arteries) in the human body which have similar visual appearances, so the differentiation
of them based on only visual expression via endoscopy is a challenging task to the
medical experts. To support keyhole-surgery using state-of-the-art image processing
solutions, we have developed a semi-automatic software which can distinguish ureters
from arteries by a dedicated convolutional neural network (CNN). We have trained the
CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones.
94.2% accuracy has been achieved in this two-classes classification task …
