用超体素损失函数实现高效连通性保持的实例分割
Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
- 将数字拓扑中的简单点概念扩展到超体素集合，设计拓扑感知损失
- 在小鼠脑3D光镜数据集上实现高精度连通性保持分割
- 适合神经连接组学中细长结构的分割任务
重建神经元及其远距离投射轴突的复杂局部形态，有助于解决神经科学中的诸多连接性问题。连接组学流程的主要瓶颈在于拓扑错误的修正，因为多个纠缠的神经树突分支构成极具挑战性的实例分割问题。更广泛地，曲线状、纤维状结构的分割仍面临重大挑战。为此，我们将数字拓扑中的简单点概念扩展至连通的体素集合（即超体素），提出一种计算开销极小的拓扑感知神经网络分割方法。该方法在新的小鼠脑3维光显微图像公开数据集上得到验证，并在基准数据集DRIVE、ISBI12和CrackTree上表现优异。
原文摘要 · Abstract (English)
Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck in connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors is a challenging instance segmentation problem. More broadly, segmentation of curvilinear, filamentous structures continues to pose significant challenges. To address this problem, we extend the notion of simple points from digital topology to connected sets of voxels (i.e. supervoxels) and propose a topology-aware neural network segmentation method with minimal computational overhead. We demonstrate its effectiveness on a new public dataset of 3-d light microscopy images of mouse brains, along with the benchmark datasets DRIVE, ISBI12, and CrackTree.
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