基于WRN-PPNet的多模态MRI脑肿瘤全自动分割

Automatic Segmentation of Multimodal MRI Brain Tumors Based on WRN-PPNet

多模态磁共振成像脑肿瘤图像存在灰度不均匀、组织类别多样等缺陷,导致脑肿瘤分割难度大、精度低,且已有脑肿瘤分割算法多为半自动分割算法。为此,建立一种端到端的全自动脑肿瘤分割模型。对脑肿瘤三维图像切片化以获得大量二维切片图像,将训练集的切片图像标准化后直接输入该分割模型,然后用训练好的模型正确分割出脑部神经胶质瘤区域,并采用Dice系数、灵敏度系数以及阳性预测率系数评估模型的分割性能。实验结果表明,该模型操作简单,鲁棒性较好,3个评估指标值分别能够达到0. 94、0. 92和0. 97。

Multimodal Magnetic Resonance Imaging(MRI)brain tumors image has many defects,such as uneven gray level,diverse tissue types,which lead to the difficulty and low accuracy of brain tumors segmentation,and most of the existing brain tumors segmentation algorithms are semi-automatic segmentation algorithms.To solve this problem,an end-to-end automatic brain tumors segmentation model is established.A large number of two-dimensional slice images are obtained by slicing the three-dimensional images of brain tumors.The slice images of the training set are normalized and then directly input into the segmentation model.The brain glioma region is correctly segmented by the trained model.The segmentation of the model is evaluated by Dice coefficient,sensitivity coefficient and Positive Predictive Value(PPV)coefficient.Experimental results show that the proposed model is easy to operate and has good robustness,the three evaluation indexes can reach 0.94,0.92 and 0.97 respectively.