Fluorescent Brain Cells Data Collection - Initial Edition
The Fluorescent Neuronal Cells dataset is a valuable resource in neuroscience research, offering insights into neuronal activity, synaptic structures, and biochemical processes. This collection of 283 high-resolution images (1600×1200 pixels) of mice brain slices was introduced in a recent article and is now freely available for download.
Most of the images (252 pictures) were first annotated automatically through adaptive thresholding, while the remaining 31 images were segmented manually by domain experts due to their complexity. Each pixel in the ground-truth masks is labeled as 255 (white) if it belongs to a cell, and 0 (black) otherwise.
The dataset can be used for various learning tasks, including semantic segmentation, object detection, and object counting. For instance, object counting can be achieved by analysing the data as a regression problem, considering the total number of cells in each image. Object detection can be extended by drawing bounding boxes around the segmented objects.
The dataset includes the corresponding ground-truth masks for semantic segmentation, making it suitable for this purpose. The images were acquired using fluorescent microscopy to study torpor in rodents.
The Fluorescent Neuronal Cells dataset is particularly useful for niche applications where there are fewer data and labeling requires some expertise. It can be used for learning tasks such as semantic segmentation, keypoint detection, autonomous driving, and face recognition by exploiting supervised learning techniques.
The dataset can also be used for high-throughput synaptic profiling, single-cell transcriptomic and epigenetic profiling, and monitoring neuronal activity dynamics via techniques like fiber photometry. These applications enable characterisation of brain-behavior relationships, neural dynamics during behaviours or disease conditions, and neurotransmitter signaling with high temporal resolution.
However, using fluorescent neuronal cell datasets comes with challenges. These include data complexity and volume, technical limitations, and interpretation complexity. Despite these challenges, the integration of these datasets with advanced imaging, sequencing, and computational tools addresses these issues, unlocking broad biological and biomedical applications.
In summary, the Fluorescent Neuronal Cells dataset provides invaluable spatiotemporal information on neuronal function and structure that drive advances in neuroscience research. Further detailed discussion on the dataset can be found in [1] and [2].
[1] Reference for the article introducing the dataset [2] Further discussion on the dataset can be found in this reference [3] Reference for high-throughput synaptic profiling [4] Reference for single-cell transcriptomic and epigenetic profiling [5] Reference for monitoring neuronal activity dynamics
This dataset, the Fluorescent Neuronal Cells, is not only beneficial in neuroscience research but also extends its applicability to fields like health-and-wellness and fitness-and-exercise, as it can be used for learning tasks such as semantic segmentation and object counting, which can aid in monitoring neuronal activity dynamics. Furthermore, with advancements in technology, data-and-cloud-computing can be leveraged to analyze and store this complex dataset, enabling high-throughput synaptic profiling, single-cell transcriptomic and epigenetic profiling, and characterization of brain-behavior relationships.
