A benchmark for Deep Learning-based approaches for In-vivo segmentation of 2D images in Total Knee Arthroplasty

Abstract

Progress in machine learning and artificial intelligence (AI) opens the way to the devel- opment of smart clinical-assistance systems and decision-support tools for the operating room (OR). Yet, before deploying these algorithms in the OR, assessment of their perfor- mances in real clinical conditions is necessary. Gathering intraoperative data for training and testing is hard, and robustness to the challenging conditions of the OR is not always demonstrated. In this paper we introduce a unique multi-patient dataset of images cap- tured during Total Knee Arthroplasty (TKA) surgery. We use this dataset to compare five deep learning-based image segmentation approaches and provide quantitative and qualita- tive results. We hope that this work will help bringing light on the performances of AI in a real surgical environment.