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Brain tissue has long been studied and detailed. Currently there exist both open source and proprietary tools to convert 2 dimensional images from stacks or voxels into 3 dimensional reconstructions. In cases where image qualities are low, or the structure of the brain has altered, images must be pre-processed before rendering can take place. We have created stand-alone models based trained on mouse brain samples that, when combined with our image processing pipeline, model retraining, and 3 dimensional rendering, improve image segmentation and component prediction by nearly 30%. In addition our rendered images are manipulate-able in real time, with component selection, zoom pan and tilt manipulation as well as luminosity and opacity controls. Additionally, to ensure the pre-trained models can be optimized for any environment, we have created a custom tool allowing for humans to interrupt the pipeline, retrain the model in real time, and re-deployed on any image database. Our pipeline is inherently scalable and is written to be deployed in a remote computer cluster environment allowing for thousands of pixels to be evaluated, modeled and modified without artificial thresholds caused by individual computing resources. Our pipeline has been tested on multiple datasets of differing qualities and initial deployment on human tissue samples has been started by our advising team. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Bond, Matthew L.; Kazemisefat, Doreh; Mondal, Ashok; Silva, Daniel; Sha, Peishan; Ellisman, Mark; Madany, Matthew; Peltier, Steve (2020). Alzheimer's Disease Data Analysis. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0B856N6
Type
dataset
Identifier
ark:/20775/bb5602691s
Language
English
Subject
Alzheimer's disease analysis Manifold object detection Cell Image Library Pacific Research Platform (PRP) Image normalization CDeep3M Voxel classification Data Science & Engineering Master of Advanced Study (DSE MAS) CycleGAN Instance segmentation DSE MAS - 2020 Cohort
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