How to create accurate training data for cell classification algorithms

Creating high-quality training data is a critical step when designing algorithms to detect and classify cells. Training data are example data points that are used to develop an algorithm. For example: a set of images, each of which might or might not contain a cell, plus human-generated tags indicating whether each image actually contains a cell.

3D labeling and imaging of Neuropeptide Y (NPY) localization in an intact mouse brain hemisphere

Figure 2: 400-µm MIP of Neuropeptide Y staining in mouse brain dorsal cortex.

Neurons communicate not only via conventional neurotransmitters but also through neuropeptides. Whereas neurotransmitters often operate by changing the excitability of neighboring neurons, neuropeptides typically impact molecular pathways within target cells leading to diverse modulatory effects. Individual neurons can release both a conventional neurotransmitter and one or more neuropeptides, providing the potential for multiple avenues of… Continue reading 3D labeling and imaging of Neuropeptide Y (NPY) localization in an intact mouse brain hemisphere