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.

翻译：全膝关节置换术中的图像分割对于精确的术前规划和准确的假体定位至关重要，可提升手术效果和患者满意度。该领域面临的核心挑战包括：精确描绘复杂解剖结构、处理图像伪影与噪声，以及开发能够应对患者常见解剖变异和病理状态的鲁棒算法。机器学习在全膝关节置换术图像分割中的应用潜力体现在其能提升分割精度、实现自动化流程，并为外科医生提供实时辅助，从而优化手术规划、假体植入及患者预后。本文提出了一种基于深度学习的鲁棒实时全膝关节置换术图像分割方法。该深度学习模型在大型数据集上训练，在精确分割植入股骨与胫骨方面表现出色，与真实标注相比实现了88.83的惊人平均精度均值（mean-Average-Precision, mAP），同时达到每秒20帧（fps）的实时分割速度。我们创新性地提出了适用于植入式膝关节荧光透视或X光图像的分割方法，在准确性与速度方面展现出卓越性能，为多种潜在扩展应用奠定了基础。