  Image segmentation in total knee arthroplasty is crucial for precise
preoperative planning and accurate implant positioning, leading to improved
surgical outcomes and patient satisfaction. The biggest challenges of image
segmentation in total knee arthroplasty include accurately delineating complex
anatomical structures, dealing with image artifacts and noise, and developing
robust algorithms that can handle anatomical variations and pathologies
commonly encountered in patients. The potential of using machine learning for
image segmentation in total knee arthroplasty lies in its ability to improve
segmentation accuracy, automate the process, and provide real-time assistance
to surgeons, leading to enhanced surgical planning, implant placement, and
patient outcomes. This paper proposes a methodology to use deep learning for
robust and real-time total knee arthroplasty image segmentation. The deep
learning model, trained on a large dataset, demonstrates outstanding
performance in accurately segmenting both the implanted femur and tibia,
achieving an impressive mean-Average-Precision (mAP) of 88.83 when compared to
the ground truth while also achieving a real-time segmented speed of 20 frames
per second (fps). We have introduced a novel methodology for segmenting
implanted knee fluoroscopic or x-ray images that showcases remarkable levels of
accuracy and speed, paving the way for various potential extended applications.
