Objectives: To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population.
Methods: The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc.
Results: When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%.
Conclusions: The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.
目的: 探究深度学习技术在中国汉族人群CT三维重建图像自动性别识别中的可靠性和准确率。方法: 收集20~85岁汉族人群骨盆CT影像学数据700例（男性350例，女性350例），将其重建为三维虚拟骨骼模型，并截取坐骨耻骨支内侧缘（medial aspect of the ischiopubic ramus，MIPR）特征区域图像。采用Inception v4作为图像识别模型，以初始化学习和迁移学习两种方式进行训练。随机选取80%的图像作为训练验证集，20%的图像作为测试集。将左右两侧MIPR图像进行单独以及合并训练。之后使用总准确率、女性准确率、男性准确率等指标进行模型的性能评价。结果: 将左右两侧MIPR图像单独进行初始化学习训练，右侧MIPR模型的总准确率为95.7%，其中女性准确率为95.7%、男性准确率为95.7%；左侧MIPR模型的总准确率为92.1%，其中女性准确率为88.6%、男性准确率为95.7%。将左右两侧MIPR图像合并以初始化学习进行训练，模型的总准确率为94.6%，其中女性准确率为92.1%、男性准确率为97.1%。将左右两侧MIPR图像合并以迁移学习进行训练，模型的总准确率为95.7%，其中女性准确率为95.7%，男性准确率为95.7%。结论: 利用Inception v4深度学习模型和迁移学习算法对中国汉族人群骨盆MIPR图像构建性别推断模型，可对成人骨骼遗骸开展有效性别鉴定，具有较高的准确率及泛化能力。.
Keywords: deep learning; forensic anthropology; medial aspect of the ischiopubic ramus; pelvis; sex estimation; three- dimensional reconstruction; transfer learning.