The Artificial Intelligence-Enabled Medical Imaging:Today and Its Future

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.

基于膝关节软骨磁共振半定量评分的自动分割与分类评价

目的通过机器学习大量影像诊断明确的膝关节软骨不同程度病变的磁共振图像,实现膝关节软骨评分的自动分类和分割并评价其准确性,具有监测骨关节炎(osteoarthritis,OA)发生和发展过程的应用价值。材料与方法回顾性纳入590例膝关节磁共振3.0 T数据。自动分类数据557例;自动分割数据33例,其中27个数据用于训练,6个数据用于测试。在深度学习的基础上利用迁移学习方法,基于两种神经网络模型,即V型网络和Inception网络,进行自动分割和分类。软骨的分割由放射科医师进行手工标注,以进行比较。软骨的分类由放射科医师依据全器官磁共振成像评分(whole-organ magnetic resonance imaging score,WORMS)分区方法及Recht评分标准进行评分;自动分割精度的量化指标使用Dice相似性系数。结果所有膝关节骨骼自动分割的Dice相似性系数值(dice similarity coefficient value,DSC)均在0.90以上,膝关节软骨自动分割的Dice相似性系数值均在0.70以上,表明本研究提出的自动分割方法能准确地分割膝关节骨骼和软骨。结论利用深度神经网络建立了一种膝关节自动分割和软骨病变分类的方法,V型网络和Inception网络展示了快速生成准确分割和分类结果的效率和精确度,可以得到较准确的结果,有助于提取形态特征,具有监测和诊断OA的价值,为影像诊疗流程自动化提供了技术支持。