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首页> 外文期刊>Medical Imaging, IEEE Transactions on >Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features
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Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

机译：具有学习形状特征的EM图像堆栈中基于超素的线粒体分割

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It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degenerative diseases is strongly suspected. Electron microscopy (EM), with its very high resolution in all three directions, is one of the key tools to look more closely into these issues but the huge amounts of data it produces make automated analysis necessary. State-of-the-art computer vision algorithms designed to operate on natural 2-D images tend to perform poorly when applied to EM data for a number of reasons. First, the sheer size of a typical EM volume renders most modern segmentation schemes intractable. Furthermore, most approaches ignore important shape cues, relying only on local statistics that easily become confused when confronted with noise and textures inherent in the data. Finally, the conventional assumption that strong image gradients always correspond to object boundaries is violated by the clutter of distracting membranes. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates shape features capable of describing the 3-D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries. Our experiments demonstrate that our approach is able to segment mitochondria at a performance level close to that of a human annotator, and outperforms a state-of-the-art 3-D segmentation technique.
机译：越来越清楚的是，线粒体在神经功能中起着重要的作用。最近的研究表明线粒体形态对于细胞生理和突触功能至关重要，强烈怀疑线粒体缺陷与神经退行性疾病之间存在联系。电子显微镜（EM）在所有三个方向上都具有很高的分辨率，是更仔细地研究这些问题的关键工具之一，但是它产生的大量数据使自动分析成为必要。由于多种原因，设计用于自然2D图像的最新计算机视觉算法在应用于EM数据时往往表现不佳。首先，典型EM卷的绝对大小使大多数现代分割方案难以处理。此外，大多数方法都忽略了重要的形状提示，仅依靠局部统计数据，当遇到数据固有的噪声和纹理时，这些统计数据很容易混淆。最后，分散膜的混乱破坏了传统的假设，即强图像梯度始终对应于对象边界。在这项工作中，我们提出了一种自动图形分区方案来解决这些问题。它通过在超级体素而不是体素上进行操作来降低计算复杂性，并具有能够描述目标对象的3D形状的形状特征，并学会识别真实边界的独特外观。我们的实验表明，我们的方法能够以接近人类注释者的性能水平分割线粒体，并且优于最新的3D分割技术。

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