According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising.

中文翻译：

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使用自适应 U-Net 和局部卷积神经网络从脑磁共振成像中分割白质、灰质和脑脊液

根据 2015 年世界阿尔茨海默病报告，全球有 4600 万人患有痴呆症。疾病的诊断有助于医生更好地治疗病人。疾病的迹象之一与白质、灰质和脑脊液有关。因此，脑成像中的三个组织的自动分割，尤其是磁共振成像（MRI）在医学分析中起着重要作用。在这项研究中，我们提出了一种在三维 (3D) 脑 MRI 中自动分割这些组织的有效方法。首先，使用深度学习模型来分割确定区域和不确定区域。在不确定区域，使用另一个深度学习模型对每个像素进行分类。在实验中，使用自适应 U-net 模型来分割确定区域和不确定区域，具有多个输入的局部卷积神经网络（CNN）模型仅用于对不确定区域中的每个像素进行分类。我们的方法使用真实图像数据库、Internet Brain Segmentation Repository 数据库进行了评估，有 18 人 (IBSR 18) (https://www.nitrc.org/projects/ibsr)，并与最先进的方法进行比较，结果非常有希望.