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Image segmentation in total knee arthroplasty is crucial for precisepreoperative planning and accurate implant positioning, leading to improvedsurgical outcomes and patient satisfaction. The biggest challenges of imagesegmentation in total knee arthroplasty include accurately delineating complexanatomical structures, dealing with image artifacts and noise, and developingrobust algorithms that can handle anatomical variations and pathologiescommonly encountered in patients. The potential of using machine learning forimage segmentation in total knee arthroplasty lies in its ability to improvesegmentation accuracy, automate the process, and provide real-time assistanceto surgeons, leading to enhanced surgical planning, implant placement, andpatient outcomes. This paper proposes a methodology to use deep learning forrobust and real-time total knee arthroplasty image segmentation. The deeplearning model, trained on a large dataset, demonstrates outstandingperformance in accurately segmenting both the implanted femur and tibia,achieving an impressive mean-Average-Precision (mAP) of 88.83 when compared tothe ground truth while also achieving a real-time segmented speed of 20 framesper second (fps). We have introduced a novel methodology for segmentingimplanted knee fluoroscopic or x-ray images that showcases remarkable levels ofaccuracy and speed, paving the way for various potential extended applications.

An Automated Real-Time Approach for Image Processing and Segmentation of Fluoroscopic Images and Videos Using a Single Deep Learning Network