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首页> 美国卫生研究院文献>other >A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation
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A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation

机译：修改后的U-Net卷积网络具有基于最近邻居重采样的弹性变换用于脑组织表征和分割

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The detection and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is a very challenging task, despite the availability of modern medical image processing tools. Neuro-radiologists still diagnose deadly brain cancers such as even glioblastoma using manual segmentation. This approach is not only tedious, but also highly variable, featuring limited accuracy and precision, and hence raising the need for more robust, automated techniques. Deep learning methods such as the U-Net deep convolutional neural networks have been widely used in biomedical image segmentation. Although this model was demonstrated to yield desirable results on the BRATS 2015 dataset by using a pixel-wise segmentation map of the input image as an auto-encoder, which assures best segmentation accuracy, the output only showed limited accuracy and robustness for a number of cases. The goal of this work was to improve the U-net model by replacing the de-convolution component with an up-sampled by the Nearest-neighbor algorithm and also employing an elastic transformation to augment the training dataset to render the model more robust, especially for the segmentation of low-grade tumors. The proposed Nearest-Neighbor Re-sampling Based Elastic-Transformed (NNRET) U-net Deep CNN framework has been trained on 285 glioma patients BRATS 2017 MR dataset available through the MICCAI 2017 grand challenge. The framework has been tested on 146 patients using Dice similarity coefficient (DSC) & Intersection over Union (IoU) performance metrics and outweighed the classic U-net model.
机译：尽管有现代医学图像处理工具，但通过磁共振成像（MRI）进行脑肿瘤的检测和分割是一项非常具有挑战性的任务。神经放射学家仍然使用手动分割诊断致命的脑癌，甚至胶质母细胞瘤。这种方法不仅繁琐，而且变化很大，准确性和精确度有限，因此需要更强大，更自动化的技术。诸如U-Net深度卷积神经网络之类的深度学习方法已广泛用于生物医学图像分割中。尽管通过使用输入图像的逐像素分割图作为自动编码器证明了该模型在BRATS 2015数据集上产生了令人满意的结果，从而确保了最佳的分割精度，但对于许多案件。这项工作的目的是通过用最近邻算法上采样来代替去卷积组件，并采用弹性变换来增强训练数据集，以使模型更加健壮，从而改善U-net模型。用于低度肿瘤的分割。拟议中的基于近邻重采样的弹性变换（NNRET）U-net深度CNN框架已通过MICACAI 2017挑战赛获得的285名胶质瘤患者BRATS 2017 MR数据集进行了培训。该框架已使用Dice相似系数（DSC）和联合交叉口（IoU）性能指标在146位患者上进行了测试，并且胜过了经典的U-net模型。

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