Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for Large-Scale Calcium Imaging Datasets.
Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for Large-Scale Calcium Imaging Datasets.
复制标题
DOI：
10.1523/eneuro.0056-18.2018
复制
发表时间：
2018-09
期刊：
影响因子：
3.4
通讯作者：
Han X
中科院分区：
文献类型：
--
作者：
Shen SP;Tseng HA;Hansen KR;Wu R;Gritton HJ;Si J;Han X
钙成像的进步使得同时记录越来越多的神经元成为可能。神经科学家现在可以对数百到数千个单个神经元进行常规成像。与大量单个神经元成像的进步并行的一个新兴技术挑战是相应大型数据集的处理。一个重要的步骤是识别单个神经元。传统方法主要依赖于手动或半手动检查，无法扩展以处理大型数据集。为了应对这一挑战，我们专注于开发一种自动分割方法，我们将其称为自适应阈值自动细胞分割（ACSAT）。 ACSAT 使用时间折叠图像，并包含一个迭代过程，该过程根据图像像素强度的分布在连续迭代期间自动计算全局和局部阈值。因此，该算法能够处理不同钙成像数据集中形态细节和荧光强度的变化。在本文中，我们通过在 500 个模拟数据集、两个宽视场海马数据集、一个宽视场纹状体数据集、一个宽视场细胞培养数据集和一个双光子海马数据集上进行测试来展示 ACSAT 的实用性。对于真实的模拟数据集，当信噪比不低于~24 dB 时，ACSAT 实现了 >80% 的召回率和精度。
Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.