Computer Assisted Instruction in Laparoscopic Surgery using Deep Learning

Abstract

Minimally invasive surgery (MIS) is famous to cause less harm to the skin compared with regular operation, due to the tiny surgical instruments and the small incisions used. It provides many advantages to the patients like a shorter hospital stays, reduced pain and faster recovery. In addition, MIS offers the possibility of video record the surgery. These videos are used for teaching purposes, evaluating surgeons and also they are treated as evidence in case of lawsuits from patients. On the other hand, these types of surgeries are difficult to learn and teach. That’s why surgeons tend to check MIS videos for a possible technical error. Since MIS medias are commonly very long, this manual surgical quality assessment (SQA) process, without any support of video search, take so much time and effort. To surmount this issue, we present a neural network based solution, to identify surgical instruments and index these videos, using three fine-tuned Convolutional Neural Network VGG19, Inception v-4 and NASNet-A. Finally, we present the benefits of the proposed approach on the Cholec80 dataset.