基于尺度注意力沙漏网络的头部MRI解剖点自动定位

Automatic location of anatomical points in head MRI based on the scale attention hourglass network

为了自动定位头部核磁共振成像(Magnetic Resonance Imaging,MRI)中稳定解剖结构点,提出了一种基于沙漏网络(Hourglass Network,HN)的头部MRI图像解剖点自动定位方法。该方法使用HN的基本结构实现多尺度特征的提取与融合,并在融合多尺度特征时引入尺度注意力机制,提高解剖结构点的提取精度。利用DSNT(Differentiable Spatial to Numerical Transform)层将卷积神经网络生成的预测热图进行坐标回归来定位解剖点。500例头部MRI图像用于训练,300例图像用于测试,测试结果表明本文提出的方法对四个解剖点的定位准确率均超过了80%,与常用的关键点定位方法相比,该方法取得了最好的效果。该方法可辅助医生进行图像的解剖点标记,为头部图像的自动配准和头部疾病的大数据分析提供技术支持。

To automate the location of stable anatomical points in head magnetic resonance imaging(MRI),an automated anatomical point locating procedure using head MRI images has been proposed that relies on hourglass network(HN).In this method,the basic HN structure is used to extract and fuse multi-scale features.The scale attention mechanism is introduced in the fusion of multi-scale features to improve anatomical point location accuracy.This method uses the differential spatial to numerical trans⁃form(DSNT)layer to locate anatomical points using coordinate regression of the predicted heat map gen⁃erated by the convolution neural network.Five hundred head MRI images were used for training,whereas three hundred images were used for testing.Accuracy of the proposed method for location of four anatomical points was>80%.Compared with the common methods currently used to locate key points,the pro⁃posed method achieved the best results.This method can assist doctors in marking anatomical points in im⁃ages and provide technical support for automated registration of head MRI and big data analyses of head diseases.