基于 Mask RCNN 的桥小脑角区脑膜瘤与听神经瘤分类定位研究

由于人体桥小脑角区的脑膜瘤与听神经瘤在影像学的表现以及发病位置极其相似，所以临床诊断极易发生误诊．针对此问题，本文应用掩膜区域卷积神经网络（Mask RCNN）对两类肿瘤进行分类定位研究．首先采集89名脑膜瘤与218名听神经瘤患者的T₁WI-SE序列的磁共振图像，对其进行预处理，再结合改进的特征金字塔网络（FPN）算法进行网络训练．本文对比了三种不同的Mask RCNN主干网络对两者分类定位的效果．结果表明，结合改进的FPN算法和ResNet101作为主干网络的Mask RCNN分类定位模型能够有效实现对两类肿瘤的分类定位，精确率为0.918 2、召回率为0.856 9、特异性为0.876 2、均值平均精度（mAP）为0.90．

Classification and Localization of Meningioma and Acoustic Neuroma in Cerebellopontine Angle Based on Mask RCNN

Differential diagnosis of meningioma and acoustic neuroma can be difficult because these two tumors have similar locations and appearances on medical images.To address this problem,mask region convolutional neural network(Mask RCNN)was used to classify and diagnose those two types of tumors.First,magnetic resonance images acquired with T1-weighted spin-echo(T1WI-SE)sequence of 89 meningioma and 218 acoustic neuroma patients were collected and preprocessed.Then the improved feature pyramid networks(FPN)algorithm was used for model network training.The effects of three different backbone feature extraction layers on classification and location were compared.It was demonstrated that Mask RCNN model with improved FPN and ResNet101 as backbone network is able to effectively classify and locate meningioma and acoustic neuroma,the values of precision,recall,specificity,and mean average precision(mAP)are 0.9182,0.8569,0.8762,and 0.90,respectively.