基于人工智能的正位DR胸片质控体系研究与应用

Research and application of orthotopic DR chest radiograph quality control system based on artificial intelligence

随着医学诊断、治疗模式的改变,医学影像的质量直接影响着医生对病情的诊断和治疗。因此,通过计算机实现智能影像质控对放射科技师的拍片工作会有较大的辅助作用。本文拟就深度学习领域中的图像分割模型、图像分类模型结合传统图像处理算法应用于医学影像质量评价的研究方法及应用情况予以阐述。我们发现使用深度学习算法对医学影像大数据进行有效训练,提取出来的特征相比于单纯使用传统图像处理算法更加准确、高效,诠释了深度学习在医疗领域的广阔应用前景。本文开发出了一套辅助拍片智能质控系统,并成功应用到了华西医院和其他市、县级医院的放射科,有效验证了该质控系统的可行性与稳定性。

With the change of medical diagnosis and treatment mode,the quality of medical image directly affects the diagnosis and treatment of the disease for doctors.Therefore,realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer’s filming work.In this paper,the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described.The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data,which explains the broad application prospect of deep learning in the medical field.This paper developed a set of intelligent quality control system for auxiliary filming,and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals,which effectively verified the feasibility and stability of the quality control system.