#Stereology based counting technique manual

Here, we propose multiple approaches to generate and leverage unlabeled data for training deep learning models. Therefore obtaining labeled data remains a bottleneck for effective supervised leaning based models. However, labeling data is time-consuming, labor-intensive, and requires expert knowledge in many fields, such as neuroscience. These methods are based on supervised deep learning, which requires large labeled data sets for training in order to learn to count cells effectively. This dissertation provides deep learning methods to automate unbiased stereology cell counting in microscopy images of mice stained brain sections. Thus, the lack of high-throughput technology for automating unbiased stereology analyses remains a major obstacle to further progress in a wide range of neuroscience and cancer sub-disciplines.

#Stereology based counting technique manual

However, this human-based manual approach is time-consuming, labor-intensive, subject to human errors, recognition bias, fatigue, variable training, poor reproducibility, and inter-observer error. Unfortunately, in unbiased stereology current practices rely on a well-trained human to manually count hundreds of cells in microscopy images.

The gold-standard for quantifying cells in tissue sections is the unbiased stereology approach. Cell quantification in histopathology images plays a significant role in understanding and diagnosing diseases such as cancer and Alzheimers.