Gliomas are the most common malignant brain tumors with different grades that highly determine the rate of survival in patients. Tumor segmentation and grading using magnetic resonance imaging (MRI) are common and essential for diagnosis and treatment planning. To achieve this clinical need, a deep learning approach that combines convolutional neural networks (CNN) based on the U-net for tumor segmentation and transfer learning based on a pre-trained convolution-base of Vgg16 and a fully connected classifier for tumor grading was developed. The segmentation and grading models use the same pipeline of T1-precontrast, fluid attenuated inversion recovery (FLAIR), and T1-postcontrast MRI images of 110 patients of lower-grade glioma (LGG) for training and evaluations. The mean dice similarity coefficient (DSC) and tumor detection accuracy achieved by the segmentation model are 0.84 and 0.92, respectively. The grading model classifies LGG into grade II and grade III with accuracy, sensitivity, and specificity of 0.89, 0.87, and 0.92, respectively at the MRI images' level and 0.95, 0.97, and 0.98 at the patients’ level. This work demonstrates the potential of using deep learning in MRI images to provide a non-invasive tool for simultaneous and automated tumor segmentation, detection, and grading of LGG for clinical applications.

神经胶质瘤是最常见的具有不同等级的恶性脑肿瘤，在很大程度上决定了患者的存活率。使用磁共振成像（MRI）进行肿瘤分割和分级对于诊断和治疗计划来说是必不可少的。为了满足这一临床需求，一种深度学习方法结合了基于U-net的卷积神经网络（CNN）进行肿瘤分割，并基于预训练的Vgg16卷积基础和完全连接的分类器将转移学习用于肿瘤分级。发达。分割和分级模型使用110例低级神经胶质瘤（LGG）患者的T1造影前对比，液体衰减反转恢复（FLAIR）和T1造影后MRI图像进行相同的训练和评估。分割模型获得的平均骰子相似系数（DSC）和肿瘤检测精度分别为0.84和0.92。该分级模型将LGG分为II级和III级，其准确性，敏感性和特异性在MRI图像级别分别为0.89、0.87和0.92，在患者级别分别为0.95、0.97和0.98。这项工作证明了在MRI图像中使用深度学习为临床应用中LGG的同时和自动肿瘤分割，检测和分级提供非侵入性工具的潜力。