Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.
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DOI：
10.1109/tmi.2021.3097826
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发表时间：
2021-12
影响因子：
10.6
通讯作者：
Seung HS
中科院分区：
文献类型：
--
作者：
Lee K;Lu R;Luther K;Seung HS
我们表明，通过深度度量学习学习的密集体素嵌入可以用于从3D电子显微镜图像中产生高度准确的神经元分割。在体素之间的一组边缘上的“度量图”由卷积网络生成的密集体素嵌入构造。分割的度量图与长程边缘排斥约束产生一个初始分割精度高，非常薄的对象具有很大的精度增益。卷积嵌入网络在没有任何修改的情况下被重用，以聚集由复杂的“自接触”图案引起的系统分裂。我们所提出的方法实现了最先进的精度上的挑战性问题的三维神经元重建从连续切片电子显微镜获得的脑图像。我们的替代方案，以对象为中心的表示可以更普遍地用于自动神经回路重建中的其他计算任务。
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A “metric graph” on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network. Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex “self-contact” motifs. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.