Department of Electronic Engineering, Fudan University, Shanghai, China.
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China.
J Healthc Eng. 2017;2017:9283480. doi: 10.1155/2017/9283480. Epub 2017 Jun 13.
This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.
这项工作提出了一种新颖的自动三维（3D）磁共振成像（MRI）分割方法，该方法将广泛应用于最常见和最具侵袭性的脑肿瘤，即神经胶质瘤的临床诊断。该方法结合了多路径卷积神经网络（CNN）和全连接条件随机场（CRF）。首先，将 3D 信息引入 CNN，从而更准确地识别对比度低的神经胶质瘤。然后，添加全连接 CRF 作为后处理步骤，以更精细地描绘神经胶质瘤边界。该方法应用于 160 例低级别神经胶质瘤患者的 T2flair MRI 图像。使用 59 例数据进行训练和手动分割作为金标准，我们的方法对 101 例 MRI 图像的测试集的 Dice 相似系数（DSC）为 0.85。我们的方法的结果优于另一种最先进的 CNN 方法，该方法在同一数据集上获得的 DSC 为 0.76。这证明了我们的方法可以产生更好的低级别神经胶质瘤分割结果。
