Automated glioma grading on conventional MRI images using deep convolutional neural networks

作者：Ying Zhuge, Holly Ning, Peter Mathen, Jason Y. Cheng, Andra Valentina Krauze, Kevin A. Camphausen, Robert W. Miller · 发表于：Medical Physics · 年份：2020 · DOI：10.1002/mp.14168 · 被引用次数：168 · 研究领域：Brain Tumor Detection and Classification、Glioma Diagnosis and Treatment、Advanced Neural Network Applications

PURPOSE: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). METHODS: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. RESULTS: The proposed schemes were evaluated on The Cancer Imaging Archive (T...