Image segmentation for neuroscience: lymphatics
Image segmentation for neuroscience: lymphatics
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DOI：
10.1088/2515-7647/ac050e
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
N. Tabassum;J. Wang;M. Ferguson;J. Herz;M. Dong;A. Louveau;J. Kipnis;S. Acton
中科院分区：
文献类型：
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作者：
N. Tabassum;J. Wang;M. Ferguson;J. Herz;M. Dong;A. Louveau;J. Kipnis;S. Acton
最近神经科学的一项发现提示了图像分析创新的必要性。神经科学家已经发现了脑膜淋巴管在大脑中的存在，并在阿尔茨海默病小鼠模型中显示了它们在预防认知能力下降方面的重要性。随着年龄的增长，淋巴管变窄，脑脊液排出不畅，导致斑块积聚，这是阿尔茨海默病的标志。在目前的实践中，血管边界和宽度的检测是手工进行的，因此存在高错误率和潜在的观察者偏差。现有的血管分割方法依赖于用户定义的初始化，这是耗时的，并且由于大量的背景杂波和噪声而难以在实践中实现。这项工作提出了一种以分层抠像为特征的水平集分割方法LyMPhi，以预先确定前景和背景区域。水平集力场由抠图计算的前景信息调制，同时也约束分割轮廓平滑。与竞争算法相比，该方法的分割输出具有更高的整体Dice系数和边界F1分数。我们的新的形状变形为基础的方法产生的真实的和合成数据的算法进行了测试。与现有的水平集分割方法相比，LyMPhi在不同的初始条件下也更稳定。最后，对人工分词进行统计分析，以证明三个注释器之间的变化和分歧。
A recent discovery in neuroscience prompts the need for innovation in image analysis. Neuroscientists have discovered the existence of meningeal lymphatic vessels in the brain and have shown their importance in preventing cognitive decline in mouse models of Alzheimer’s disease. With age, lymphatic vessels narrow and poorly drain cerebrospinal fluid, leading to plaque accumulation, a marker for Alzheimer’s disease. The detection of vessel boundaries and width are performed by hand in current practice and thereby suffer from high error rates and potential observer bias. The existing vessel segmentation methods are dependent on user-defined initialization, which is time-consuming and difficult to achieve in practice due to high amounts of background clutter and noise. This work proposes a level set segmentation method featuring hierarchical matting, LyMPhi, to predetermine foreground and background regions. The level set force field is modulated by the foreground information computed by matting, while also constraining the segmentation contour to be smooth. Segmentation output from this method has a higher overall Dice coefficient and boundary F1-score compared to that of competing algorithms. The algorithms are tested on real and synthetic data generated by our novel shape deformation based approach. LyMPhi is also shown to be more stable under different initial conditions as compared to existing level set segmentation methods. Finally, statistical analysis on manual segmentation is performed to prove the variation and disagreement between three annotators.