用贝叶斯深度学习自动分割4D荧光成像中的神经元活动，提升效率与可靠性。
Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach
- 结合时空信息的贝叶斯框架，通过相关图与均值图像实现精准检测。
- 在合成真值上达到0.81的平均骰子系数，重复实验间一致性达0.79。
- 适合需要快速分析大规模神经活动的行为研究者使用。
荧光显微钙成像是在活体中以单细胞分辨率同步记录和分析大规模神经元活动的基础工具。从记录数据中自动精确识别与行为相关的神经元活动，对研究生物大脑活动映射至关重要。然而，该问题长期受限于耗时耗力且泛化能力差的手动分割。为此，我们提出一种贝叶斯深度学习框架，用于光片显微镜获取的4D时空数据中神经元活动的检测。该方法通过计算像素级时间相关图融合时间信息，并结合均值概览图的空间信息。贝叶斯框架不仅能生成概率分割图，还可建模活性神经元检测的不确定性。为评估准确性，我们实施了可重复性测试，验证网络在检测神经元活动上的泛化能力。结果表明，相对于由Otsu方法生成的合成真值，网络平均骰子系数为0.81；在两次运行之间，平均骰子系数为0.79。本方法部署后可实现活跃神经元活动的快速检测，适用于行为学研究。
原文摘要 · Abstract (English)
Fluorescence Microcopy Calcium Imaging is a fundamental tool to in-vivo record and analyze large scale neuronal activities simultaneously at a single cell resolution. Automatic and precise detection of behaviorally relevant neuron activity from the recordings is critical to study the mapping of brain activity in organisms. However a perpetual bottleneck to this problem is the manual segmentation which is time and labor intensive and lacks generalizability. To this end, we present a Bayesian Deep Learning Framework to detect neuronal activities in 4D spatio-temporal data obtained by light sheet microscopy. Our approach accounts for the use of temporal information by calculating pixel wise correlation maps and combines it with spatial information given by the mean summary image. The Bayesian framework not only produces probability segmentation maps but also models the uncertainty pertaining to active neuron detection. To evaluate the accuracy of our framework we implemented the test of reproducibility to assert the generalization of the network to detect neuron activity. The network achieved a mean Dice Score of 0.81 relative to the synthetic Ground Truth obtained by Otsu's method and a mean Dice Score of 0.79 between the first and second run for test of reproducibility. Our method successfully deployed can be used for rapid detection of active neuronal activities for behavioural studies.
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