Automated glioma grading on conventional MRI images using deep convolutional neural networks.
Automated glioma grading on conventional MRI images using deep convolutional neural networks.
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DOI：
10.1002/mp.14168
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发表时间：
2020-07
期刊：
影响因子：
3.8
通讯作者：
Miller RW
中科院分区：
文献类型：
--
作者：
Zhuge Y;Ning H;Mathen P;Cheng JY;Krauze AV;Camphausen K;Miller RW
神经胶质瘤是大脑的最常见原发性肿瘤，并根据其侵入性的组织学表现分为世界卫生组织（WHO）的I-IV。传统的MRI图像通过使用深卷积神经网络（CNN）。
所有MRI图像首先是通过刚性图像注册和强度不均匀性校正的，这两个方法由两个步骤组成：（a）基于流行的U-NET模型的三维（3D）脑肿瘤分割。肿瘤分级。分级模型是实施了二维（2D）数据增强，以增加第二种方法的训练图像的数量和多样性。
对癌症成像档案（TCIA）的低级胶质瘤（LGG）数据进行了评估，多模式的脑肿瘤图像分割（Brats）基准训练数据集具有五倍的交叉验证。 935 （敏感性），0.972（特异性）和0.963（准确性），基于2D掩码R-CNN的方法，0.947（灵敏度），0.968（特异性）（特异性）和0.971（精度）（准确性）（精度）（精度）分别用于3D Brine thy Datient of 3D DATION，而对于3D的效率，则为3D。用于测试典型图像的大小为160 ×216×176。对于基于2D掩码的肿瘤分级，该程序大约需要4小时，用于训练约60 000次迭代，用于测试2D切片的图像，大小为128×128。对于基于3DConvnet的肿瘤毕业，该程序用于2小时，用于训练2小时，用于2小时的训练，大约需要10 000次i000次尺寸，以进行64次44 s sigess和0.2 s sigpting和0.25 s sigess和0.25 s Dell Precision Tower T7910，带有两个NVIDIA TITAN XP GPU。
使用深卷积神经网络的传统MRI图像进行了两种有效的胶质瘤分级方法。
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). 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 perfor-mance 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. The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.
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影响因子：
3.9
作者：
Arvinda, H. R.;Kesavadas, C.;Nair, S.
通讯作者：
Nair, S.
影响因子：
7.6
作者：
Chen, Wei;Liu, Boqiang;Qiao, Xu
通讯作者：
Qiao, Xu
NiftyNet：医学成像深度学习平台。
影响因子：
6.1
作者：
Gibson E;Li W;Sudre C;Fidon L;Shakir DI;Wang G;Eaton-Rosen Z;Gray R;Doel T;Hu Y;Whyntie T;Nachev P;Modat M;Barratt DC;Ourselin S;Cardoso MJ;Vercauteren T
通讯作者：
Vercauteren T
影响因子：
5.7
作者：
Chen, Hao;Dou, Qi;Heng, Pheng-Ann
通讯作者：
Heng, Pheng-Ann
DOI：
10.1016/j.nicl.2014.08.001
发表时间：
2014
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Inano R;Oishi N;Kunieda T;Arakawa Y;Yamao Y;Shibata S;Kikuchi T;Fukuyama H;Miyamoto S
通讯作者：
Miyamoto S