Abstract：
BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA).
METHODS: The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.
RESULTS: Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.
CONCLUSIONS: DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.
METHODS: The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.
RESULTS: Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.
CONCLUSIONS: DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.
摘要：
背景：本研究旨在开发一种称为双路径双注意转换器（DDA-Transformer）的新型深度卷积神经网络，旨在实现精确，快速的膝关节CT图像分割，并在机器人辅助的全膝关节置换术（TKA）中进行验证。
方法：股骨，胫骨,髌骨,和腓骨分割的性能和速度进行了评估，组件尺寸的准确性，在临床上验证了使用该深度学习网络构建的机器人辅助TKA系统的骨切除和对齐.
结果：总体而言，DDA-Transformer在Dice系数方面优于其他六个网络，在联合上相交，平均表面距离，和Hausdorff距离.DDA-Transformer的分割速度明显快于nnUnet，TransUnet和3D-Unet（p<0.01）。此外，机器人辅助TKA系统在手术准确性方面优于手动组.
结论：DDA-Transformer在膝关节分割中显示出显着提高的准确性和鲁棒性，这种方便稳定的膝关节CT图像分割网络显著提高了TKA程序的准确性。
方法：股骨，胫骨,髌骨,和腓骨分割的性能和速度进行了评估，组件尺寸的准确性，在临床上验证了使用该深度学习网络构建的机器人辅助TKA系统的骨切除和对齐.
结果：总体而言，DDA-Transformer在Dice系数方面优于其他六个网络，在联合上相交，平均表面距离，和Hausdorff距离.DDA-Transformer的分割速度明显快于nnUnet，TransUnet和3D-Unet（p<0.01）。此外，机器人辅助TKA系统在手术准确性方面优于手动组.
结论：DDA-Transformer在膝关节分割中显示出显着提高的准确性和鲁棒性，这种方便稳定的膝关节CT图像分割网络显著提高了TKA程序的准确性。
