Tang Xiaoyong, Li Xiaohu, Gu Xuelian, Zhao Yuxuan, Liu Anchen, Liu Yutian, Tao Yurong
School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, P. R. China.
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi. 2023 Mar 15;37(3):348-352. doi: 10.7507/1002-1892.202212008.
To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling.
Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient ( ) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient.
The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation ( =0.999, <0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling.
The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model.
研究一种膝关节人工智能（AI）自动分割与建模方法，旨在提高膝关节建模效率。
随机选取3名志愿者的膝关节CT图像。在Mimics软件中对图像进行AI自动分割和手动分割及建模。记录AI自动建模时间。参照既往文献选取股骨远端和胫骨近端的解剖标志点，并计算与手术设计相关的指标。采用Pearson相关系数（ ）判断两种方法建模结果的相关性；用DICE系数分析两种方法建模结果的一致性。
自动建模和手动建模均成功构建了膝关节三维模型。AI重建每个膝关节模型所需时间分别为10.45、9.50和10.20分钟，短于既往文献中的手动建模时间[（64.73±17.07）分钟]。Pearson相关分析显示，手动分割和自动分割生成的模型之间存在强相关性（ =0.999，<0.001）。股骨的3个膝关节模型的DICE系数分别为0.990、0.996和0.944，胫骨的分别为0.943、0.978和0.981，验证了自动建模与手动建模之间的高度一致性。
Mimics软件中的AI分割方法可用于快速重建有效的膝关节模型。
