Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network  

作　　者：Yuzhao Wang Tian Bai Tong Li Lan Huang 

机构地区：[1]College of Computer Science and Technology,Jilin University,Changchun,130012,China [2]Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun,130012,China [3]Department of Orthopedics,The Second Hospital of Jilin University,Changchun,130012,China

出　　处：《Journal of Bionic Engineering》2022年第6期1816-1829,共14页仿生工程学报（英文版）

基　　金：National Natural Science Foundation of China(U21A20390);National Key Research and Development Program of China(2018YFC2001302);Development Project of Jilin Province of China(nos.20200801033GH,20200403172SF,YDZJ202101ZYTS128);Jilin Provincial Key Laboratory of Big Data Intelligent Computing(no.20180622002JC);The Fundamental Research Funds for the Central University,JLU.

摘　　要：Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of patients.With a clear bone blocks boundary,CT images have gained obvious advantages in OVFs diagnosis.Compared with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping shadows.Considering how to transfer CT imaging advantages to achieve OVFs classification in X-rays is meaningful.For this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency features.Different from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and X-ray.In the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant features.We employ five evaluation metrics to compare the proposed method with the state-of-the-art methods.The final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关 键 词：Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network Computer-aided diagnosis 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]