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

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.

• 2020

• 2020

• Bond, Matthew L.
• Kazemisefat, Doreh
• Mondal, Ashok
• Sha, Peishan
• Silva, Daniel

• Ellisman, Mark
• Madany, Matthew
• Peltier, Steve

• DSE MAS - 2020 Cohort

• Alzheimer's disease analysis
• CDeep3M
• Cell Image Library
• CycleGAN
• Data Science & Engineering Master of Advanced Study (DSE MAS)
• Image normalization
• Instance segmentation
• Manifold object detection
• Pacific Research Platform (PRP)
• Voxel classification

• data
• software
• text

• English

Related

• Is Derived From: CDeep3M source code and documentation on GitHub
• Is Derived From: CDeep3M2 source code and documentation on GitHub
• Is Derived From: CycleGAN and pix2pix in PyTorch
• References: Haberl, Matthias G., et al. "CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation." Nature methods 15.9 (2018): 677-680. https://doi.org/10.1038/s41592-018-0106-z
• References: Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2017.632
• References: Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV.2017.244

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• Bond, Matthew L.; Kazemisefat, Doreh; Mondal, Ashok; Silva, Daniel; Sha, Peishan

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2020-10-28