DeepAD: An automated and interpretable machine learning pipeline for image analyses of biomarkers in Alzheimer's disease

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研究の目的 To goal of the project is to develop an image processing pipeline that uses computational tools, including deep learning, to automate image analyses of Alzheimer's disease microscopy images.研究実施計画 For FY2022 and FY2023, the 2 goals are to use computational tools to automatically preprocess microscopy images of AD mouse brain, and to segment, quantify, and conduct morphological feature analysis of Ab plaques. I have developed image preprocessing pipelines to preprocess 2D AD images from the original collaborator, as well as 3D images from a new collaborator. I developed an automated workflow for processing gene expression images, which was accepted as a peer-reviewed conference paper; the computational tools developed for this work can be applied to the 若手 project. In collaboration with other lab members, we developed novel methods to conduct instant segmentation of sparse, punctate objects; this work was submitted as for peer-review at 2 other conferences.