In-vivo bone segmentation approach for Total Knee Arthroplasty

Abstract

Perceiving and making sense of the surgical scene during Total Knee Arthroplasty (TKA) surgery is crucial for building assistance and decision support systems for surgeons and their team. However, the need for large volumes of annotated and structured data for AI-based methods hinders the development of such tools. We hereby present a study on the use of transfer learning to train deep neural networks with scarce annotated data to automatically detect bony areas on live images. We provide quantitative evaluation results on in-vivo data, captured during several TKA procedures. We hope that this work will facilitate further developments of smart surgical assistance tools for orthopaedic surgery.