Abstract
Laparoscopic surgery is a surgical procedure performed by inserting narrow tubes into the abdomen without making large incisions in the skin. It is done with the aid of a video camera. Laparoscopic videos are affected by various distortions during surgery which lead to loss of visual quality. Identification of these distortions is the primary requisite in automated video enhancement systems used to classify the distortions correctly and accordingly select the proper algorithm to enhance video quality. In addition to high accuracy, the speed of distortion classification should be high, and the system must consider real-time conditions. This paper aims to address the issues faced by similar methods by developing a fast and accurate deep learning model for distortion classification. The dataset proposed by the ICIP2020 conference challenge was used for training and evaluation of the proposed method. This challenging dataset contains videos that have five types of distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur with four levels of intensity. This paper discusses the proposed solution which received the first prize in the ICIP2020 challenge. The solution utilized a transfer learning approach to transfer representation from the domain of natural images to the domain of laparoscopic videos. We used a pre-trained ResNet50 convolutional neural network (CNN) to extract informative features that were mapped by support vector machine (SVM) classifiers to various distortion categories. In this work, the problem of multiple distortions in the same video was formulated as a multi-label distortion classification problem. The approach of transfer learning with decision fusion was applied and was found to outperform other solutions in terms of accuracy (83%), F1 score of a single distortion (94.7%), and F1 score of single and multiple distortions (94.9%). In addition, the proposed solution can run in real time with an inference speed of 20 frames per second (FPS).
Highlights
IntroductionA. LAPAROSCOPIC SURGERY If one were to investigate the long history of Western medicine, one would discover that the first few ideas that led to the conception of minimally invasive surgery were
Precision, recall, and F1 score, are significant indicators that should be considered in validating the results of the distortion classification task
After evaluating the proposed method in terms of accuracy and F1 score, two other evaluation metrics that were used to rank the top solutions in the ICIP2020 challenge were inference time and speed
Summary
A. LAPAROSCOPIC SURGERY If one were to investigate the long history of Western medicine, one would discover that the first few ideas that led to the conception of minimally invasive surgery were. The associate editor coordinating the review of this manuscript and approving it for publication was Juan Liu. born in as early as 400 B.C. when Hippocrates described the use of a speculum for the examination of haemorrhoids [1]. Ephraim McDowell removed an ovarian tumour during the first successful elective laparotomy, i.e. an open surgery of the abdomen [2]. Laparotomies have remained the gold standard since until not very long ago [3] with some surgeons arguing for its superiority for certain procedures [4]–[6].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
