A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation.

S M Kamrul Hasan, Cristian A Linte
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引用次数: 36

Abstract

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.

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基于最近邻重采样弹性变换的改进U-Net卷积网络用于脑组织表征和分割。
尽管有现代医学图像处理工具,但从磁共振成像(MRI)中检测和分割脑肿瘤是一项非常具有挑战性的任务。神经放射科医生仍然使用人工分割来诊断致命的脑癌,甚至是胶质母细胞瘤。这种方法不仅乏味,而且变化很大,具有有限的准确性和精密度,因此需要更健壮的自动化技术。深度学习方法如U-Net深度卷积神经网络已广泛应用于生物医学图像分割。尽管该模型通过使用输入图像的像素分割图作为自动编码器在BRATS 2015数据集上产生了理想的结果,从而确保了最佳的分割精度,但在许多情况下,输出仅显示出有限的准确性和鲁棒性。这项工作的目标是改进U-net模型,通过最近邻算法用上采样替换去卷积分量,并采用弹性变换来增强训练数据集,使模型更加鲁棒,特别是对于低级别肿瘤的分割。提出的基于最近邻重新采样的弹性转换(NNRET) U-net深度CNN框架已在285例胶质瘤患者BRATS 2017 MR数据集上进行了训练,这些数据集可通过MICCAI 2017大挑战获得。该框架已在146名患者身上进行了测试,使用Dice相似系数(DSC)和交集超过联盟(IoU)性能指标,并优于经典的U-net模型。
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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. LEFT VENTRICULAR EJECTION FRACTION: COMPARISON BETWEEN TRUE VOLUME-BASED MEASUREMENTS AND AREA-BASED ESTIMATES.
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