权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

DeepAD：一种自动化且可解释的机器学习管道，用于阿尔茨海默病生物标志物的图像分析

基本信息

批准号：
22K15658
负责人：
Poon CharissaTingAmanda
金额：
$ 2.83万
依托单位：
Institute of Physical and Chemical Research
依托单位国家：
日本
项目类别：
Grant-in-Aid for Early-Career Scientists
财政年份：
2022
资助国家：
日本
起止时间：
2022-04-01 至 2026-03-31
项目状态：
未结题

项目摘要

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.

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. 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 Ruoshou 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.

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain

用于创建成年狨猴大脑中原位杂交基因表达数据图谱的自动化管道

DOI：
发表时间：
2023
期刊：
International Symposium on Biomedical Imaging
影响因子：
0
作者：
Poon;Charissa; Rachmadi;Muhammad Febrian; Byra;Michal; Schlachter;Matthias; Xu;Binbin; Shimogori;Tomomi; Skibbe;Henrik
通讯作者：
Henrik

Semi-supervised semantic segmentation of in situ hybridization gene expression in the marmoset brain

狨猴大脑原位杂交基因表达的半监督语义分割