Abstract：
Recent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand. To overcome these issues, we developed CalciSeg, a data-driven and reproducible approach for unsupervised functional calcium imaging data segmentation. CalciSeg addresses the challenges associated with brain structure variability and user bias by offering a computationally efficient solution for automatic image segmentation based on two parameters: regions\' size limits and number of refinement iterations. We evaluated CalciSeg efficacy on datasets of varied complexity, different insect species (locusts, bees, and cockroaches), and imaging systems (wide-field, confocal, and multiphoton), showing the robustness and generality of our approach. Finally, the user-friendly nature and open-source availability of CalciSeg facilitate the integration of this algorithm into existing analysis pipelines.
摘要：
钙成像的最新进展，包括开发快速和敏感的基因编码指标，用于宽场成像的高分辨率相机芯片，和激光扫描显微镜中的共振扫描镜，显着提高了功能成像分析的时间和空间分辨率。尽管如此，成像方法和大脑结构的可变性挑战了多功能和可靠的分割方法的发展。标准技术,例如手动选择感兴趣的区域或机器学习解决方案，经常由于任何一种用户偏见而达不到目标，系统之间的不可转移性，或计算需求。为了克服这些问题,我们开发了CalciSeg,一种数据驱动和可重复的无监督功能钙成像数据分割方法。CalciSeg通过提供基于两个参数的自动图像分割的计算高效解决方案来解决与大脑结构变异性和用户偏见相关的挑战：区域大小限制和细化迭代次数。我们在不同复杂性的数据集上评估了CalciSeg的功效，不同的昆虫种类(蝗虫，蜜蜂,和蟑螂)，和成像系统(宽视场，共焦,和多光子)，显示了我们方法的稳健性和一般性。最后,CalciSeg的用户友好性质和开源可用性有助于将该算法集成到现有的分析管道中。
