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Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

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SomaSeg:一个强大的神经元识别框架,用于两光子成像视频成像.

Junjie Wu¹, Hanbin Wang², Weizheng Gao²

¹College of Engineering, Peking University, Beijing, People's Republic of China.

Journal of neural engineering

|July 19, 2024

概括

SomaSeg是一个新的框架,可以准确地识别光视频中的神经元,即使有噪音和重叠的细胞. 这提高了神经元人口动态分析的可靠性.

关键词:

神经元识别神经元识别重叠实例细分的重叠实例细分.两光子成像技术视频增强功能视频增强功能

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Published on: September 16, 2022

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科学领域:

神经科学是一个神经科学.
生物医学成像技术生物医学成像技术
计算生物学计算生物学

背景情况:

准确的神经元识别对于分析神经元群体动态和从光视频中提取信号至关重要.
图像噪音,神经污染和重叠的神经元阻碍了当前的识别算法,损害了数据的可靠性.

研究的目的:

在具有挑战性的两光子成像场景中开发一个可靠的框架来准确识别神经元.
为了提高神经元形状和活动提取的可靠性.

主要方法:

开发了 SomaSeg,这是一个新级联框架,集成Duffing无声化和神经皮质污染脱雾,用于视频增强.
采用重叠实例细分网络来区分堆叠的神经元.

主要成果:

在模拟和真实实验中, SomaSeg 证明了对噪音的强度和对焦外污染的不敏感性.
该框架在复杂的成像条件下有效地处理重叠的神经元,超过了最先进的方法.

结论:

SomaSeg提供了一个广泛适用于双光子视频处理的解决方案.
该框架提高了神经元识别的可靠性,并有助于区分视觉模两可的神经元.