骨龄图像分析中的背景冗余处理方法的进展与展望OA

As a crucial method for monitoring children's growth and development and diagnosing endocrine diseases,bone age assessment is mainly performed by analyzing X-ray images of the hand and wrist.However,the application of traditional X-ray imaging technology is often affected by background interference,which reduces the accuracy of assessment results.In recent years,deep learning technology has been widely applied in the field of bone age image processing,demonstrating significant advantages due to its ability to automatically identify and extract complex features.This paper reviewed the research progress of deep learning in addressing background redundancy in bone age images,focusing on methods such as region-of-interest extraction,background segmentation technology,and attention mechanisms.These methods can effectively eliminate redundant background information and improve the accuracy and efficiency of bone age assessment.Furthermore,this paper discussed the limitations of existing technologies and future development directions,aiming to provide new research ideas and practical guidance for the field of bone age assessment.