NSF Award
2200489
Catalyst Projects provide support for Historically Black Colleges and Universities (HBCU) to work towards establishing research capacity of faculty to strengthen science, technology, engineering, and mathematics (STEM) undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. This award to Howard University supports the use of artificial intelligence and machine learning to develop improved technology for the unbiased, high throughput morphological detection and quantification of glial cells. It is anticipated that the proposed model will overcome the limitations existing methodologies, and the new deep learning system will reveal previously uncharacterized yet fundamental limitations in high-resolution segmentation of neuroglia cells.<br/><br/>Development of a novel deep learning system is proposed to resolve the difficulties in quantifying region-specific morphology and the cell subcategories in brain pathology. The proposal aims to characterize the hallmark features within glial cells, include the some and extended glial processes, which are known pathological determinants of glial cells. The approach involves a composite deep learning system with a convolutional neural network for cell detection, followed by a dedicated segmentation classifier for single cell segmentation to identify the heterogeneous glial cells with appearance of region-specific features in the background. Morphological parameters will be determined and used to predict the activation phenotypes of glial cells based on the most widely used 2D, 20X immunohistochemistry images. To enhance the model performance, a user-friendly web toolbox will be developed for fast data curation and integrated into a comprehensive database of the annotated cell morphology and phenotypes for model training and testing. The proposed model will be compared to the gold-standard manual data curated by pathology experts, and other existing computer-aided methodologies, including the rule-based semi-automatic methods, and deep learning-based methods to control for effectiveness, consistency, and computational efficiency. The automated analysis, feature-rich visualization, database integration and open-source distribution with easy access from the community, will allow this system to become a routine workflow for glial image analysis. This new technological will benefit the neuroscience research community by providing a fully automated tool for quantitative histology.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
