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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks
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Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

机译：使用卷积神经网络的脑膜显微镜图像的脑血管分割

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Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.
机译：脑血管分割是脑功能和脑病研究的图像分析的重要步骤。为了提取所有脑血管形式，包括动脉和毛细血管，一些基于滤光片的方法用于分段血管。然而，由于图像的变化和复杂性，特别是在脑血管分割中，精确和坚固的血管分割算法的设计仍然具有挑战性。在这项工作中，我们解决了由小脑显微镜获取的脑血管图像中脑微容器结构的自动和鲁棒分割问题。为了在大规模图像数据中分段微容器，我们提出了一种卷积神经网络（CNNS）架构，由具有手动标签的158万像素培训。在CNNS模型中使用了三个卷积层和一个完全连接的层。我们在每个获取的脑血管图像中提取了一系列尺寸32x32像素作为训练数据集以进入用于分类的CNN。该网络训练以输出输入贴片的中心像素属于血管结构的概率。为了构建CNNS架构，使用从商业光片荧光显微镜（LSFM）系统获取的一系列小鼠脑血管图像用于训练该模型。实验结果表明，我们的方法是有效的方法，用于有效地分割脑血管图像中的微容量结构，血管密集，不均匀的灰度和长尺度对比区域。

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