Differentiating ureter and arteries in the pelvic via endoscope using deep neural network

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 regarding a binary error function.