CRII: SCH: Analysis of Population-Based Image Metamorphosis

CRII：SCH：基于人群的图像变形分析

• 批准号: 1755970
• 负责人: Sheng Li
• 金额: $ 17.48万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755970&HistoricalAwards=false
• 关键词: CRII; SCH; Analysis; Population; Based

Uncovering brain development and disease progression can ultimately advance our understanding of brain disorders, such as mental illnesses and brain cancer. However, the analysis of early brain growth and pathological anatomy changes in brain scans needs image metamorphosis that can jointly capture structural changes occurring in a developing brain and appearance changes introduced by brain myelination and tumor growth. This project aims to develop statistical and computational models to summarize these metamorphic changes from image pairs, to sequences, and to populations. By enriching our knowledge of the healthy brain, psychopathologies, and brain pathologies, this research has the potential to provide clues in devising treatment strategies for brain disorders.The focus of this project is the development of population-based image metamorphosis that accounts for brain structure and image appearance changes simultaneously. The essential component of the developed model is an advanced image-to-image metamorphosis that allows establishing brain mappings between image pairs. This project develops a novel metamorphic regression model to handle image sequences and estimate age-related brain changes in image time-series. The population analysis component leverages the regression model and statistical tools for classification and assessment of typical and atypical brain changes. The primary applications of the developed image metamorphosis models are studies of early brain development and brain tumor growth. The resulting models could be applied to understand the etiology of developmental disorders and psychiatric conditions, such as autism, schizophrenia, and bipolar disease. They could also monitor brain tumor growth through predicting tumor evolution and classifying tumor type and grade.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.

揭示大脑发育和疾病进展最终可以提高我们对脑部疾病的理解，例如精神疾病和脑癌。然而，对脑扫描早期生长和病理解剖学的分析需要图像变形，这些变化可以共同捕获发育中的大脑和外观变化在大脑髓鞘和肿瘤生长引入的外观变化中发生的结构变化。该项目旨在开发统计和计算模型，以总结从图像对，序列到人群的这些变质变化。通过丰富我们对健康的大脑，心理病理学和大脑病理的了解，这项研究有可能为制定脑部疾病的治疗策略提供线索。该项目的重点是基于人群的图像变形的发展，这些图像变态图解了大脑结构和图像外观同时变化。开发模型的重要组成部分是一个先进的图像到图像的变形，可以在图像对之间建立大脑映射。该项目开发了一种新型的变质回归模型，以处理图像序列并估计与年龄相关的大脑图像时间序列的变化。人口分析部分利用回归模型和统计工具来分类和评估典型和非典型大脑变化。开发的图像变形模型的主要应用是对早期脑发育和脑肿瘤生长的研究。最终的模型可用于了解发育障碍和精神病患者的病因，例如自闭症，精神分裂症和双相情感疾病。他们还可以通过预测肿瘤的演化并分类肿瘤类型和等级来监测脑肿瘤的生长。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。

项目成果

HYBRID CASCADED NEURAL NETWORK FOR LIVER LESION SEGMENTATION

• DOI: 10.1109/isbi45749.2020.9098656
• 发表时间: 2020-01-01
• 期刊: 2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
• 作者: Dey, Raunak;Hong, Yi
• 通讯作者: Hong, Yi

Diffeomorphic Image Registration Using Lipschitz Continuous Residual Networks

• 发表时间: 2022
• 作者: Ankita Joshi;Yi Hong

Efficient Diffeomorphic Image Registration using Multi-Scale Dual-Phased Learning

使用多尺度双阶段学习的高效微分同胚图像配准

• DOI: 10.1109/isbi52829.2022.9761693
• 发表时间: 2022
• 期刊: IEEE 19th International Symposium on Biomedical Imaging (ISBI
• 作者: Joshi, Ankita;Hong, Yi
• 通讯作者: Hong, Yi

ASC-Net: Adversarial-Based Selective Network for Unsupervised Anomaly Segmentation

• DOI: 10.1007/978-3-030-87240-3_23
• 发表时间: 2021-01-01
• 期刊: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V
• 作者: Dey, Raunak;Hong, Yi
• 通讯作者: Hong, Yi