AUTHOR=Liu Zhi-Qiang , Hu Zi-Jian , Wu Tian-Qiong , Ye Geng-Xin , Tang Yu-Liang , Zeng Zi-Hua , Ouyang Zhong-Min , Li Yuan-Zhe
TITLE=Bone age recognition based on mask R-CNN using xception regression model
JOURNAL=Frontiers in Physiology
VOLUME=14
YEAR=2023
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1062034
DOI=10.3389/fphys.2023.1062034
ISSN=1664-042X
ABSTRACT=Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy.
Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features.
Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods.
Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.