Segmentation of glioma tumors in brain using deep convolutional neural network
Segmentation of glioma tumors in brain using deep convolutional neural network
复制标题
基于深度卷积神经网络的脑胶质瘤分割
DOI：
10.1016/j.neucom.2017.12.032
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发表时间：
2018-03-22
期刊：
影响因子：
6
通讯作者：
Majid, Muhammad
中科院分区：
文献类型：
--
作者：
Hussain, Saddam;Anwar, Syed Muhammad;Majid, Muhammad
使用基于分割的方法检测脑肿瘤在受试者的存活取决于准确和及时的临床诊断的情况下是至关重要的。胶质瘤是最常见的肿瘤，其具有不规则的形状和模糊的边界，使其成为最难检测的肿瘤之一。脑肿瘤分割的自动化仍然是一个具有挑战性的问题，主要是由于其结构的显着变化。提出了一种基于深度卷积神经网络（DCNN）的脑肿瘤自动分割算法。基于补丁的方法沿着初始模块用于通过从输入图像中提取两个不同大小的同心补丁来训练深度网络。深度神经网络的最新发展，如dropout，批量归一化，非线性激活和inception模块，用于构建一个新的ILinear nexus架构。该模块使用dropout正则化器克服了由于数据稀缺而产生的过拟合问题。在预处理步骤中对图像进行归一化和偏置场校正，然后将提取的图像块通过DCNN，DCNN将输出标签分配给每个图像块的中心像素。形态学算子用于后处理，以去除边缘周围的小误报。介绍了一种两阶段加权训练方法，并使用BRATS 2013和BRATS 2015数据集进行了评估，在类似的设置下，它提高了最先进技术的性能参数。(C)2017爱思唯尔B.V.保留所有权利。
Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors, which have irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as dropout, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using dropout regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings. (C) 2017 Elsevier B.V. All rights reserved.