Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks_2017

时间 2020-12-30

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作者:Hao Dong , Guang Yang 等训练图像被插值为1*1*1,大小为240×240×155,然后经过数据标准化(每套多模态MRI减去自己的均值,除以标准差). 肿瘤被分了4类,1.necrosis(坏死), 2.edema(水肿), 3.non-enhancing, 4.enhancing tumor.使用FLAIR图像分割完整的肿瘤区域和除水肿之外的肿瘤,使用T1c(T1-w

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1. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
2. Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
3. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
4. 3D MRI brain tumor segmentation using autoencoder regularization
5. 肝脏分割 Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
6. Brain tumor segmentation with Deep Neural Networks
7. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
8. Learning Contextual and Attentive Information for Brain Tumor Segmentation
9. Multi-task Fully Convolutional Network for Brain Tumour Segmentation
10. 【论文阅读笔记】Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Net

>>更多相关文章<<