Biomedical Image Computing and Informatics Cluster

生物医学图像计算与信息学集群

• 批准号: 9273767
• 负责人: Christos Davatzikos
• 金额: $ 194.58万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9273767
• 关键词: Algorithms; Basic Science; Biological; Characteristics; Clinical; Clinical Research; Collaborations; Collection; Complex; Computer Systems; Data; Data Set; Databases; Development; Equipment; Face; Funding; Genotype; Image; Individual; Informatics; Knowledge Discovery; Measures; Medical Genetics; Medical Imaging; Methods; Pathology; Pattern Recognition; Phenotype; Research; Resources; Scanning; Science; System; Techniques; Translational Research; United States National Institutes of Health; bioimaging; biomedical facility; computerized; data mining; design; high dimensionality; high throughput analysis; imaging study; instrument; patient population; supercomputer

Abstract The Biomedical Image Computing and Informatics Cluster (BICIC) will meet the rapidly growing needs of biomedical image computing research at Penn, and at the Center for Biomedical Image Computing in particular, and of the center's network of NIH-funded collaborating studies. Biomedical image computing faces several challenges. Increased algorithmic complexity demands computationally intensive computing and data mining of large collections of imaging, clinical and genetic data from growing patient populations is needed in order to discover biologically and clinically important relationships. These challenges underline the need for the advanced computing and storage facilities that the BICIC will provide. The proposed instrument represents an approximate 7-fold increase over the currently available resources, allowing both a more rapid execution of existing computerized analyses and providing the ability to explore methods that are currently infeasible with current equipment. The availability of this computing power in a single facility, instead of scattered resources of individual labs, will enable collaboration on algorithms, programming methods, datasets, and processing techniques that is not currently possible. The proposed server will provide a platform for developers and users of these sophisticated and demanding algorithms to push the envelope of biomedical image computing science to new levels. The proposed supercomputer will allow for high throughput analysis of scans, accelerating knowledge discovery and design of further analyses. The contribution of such a system to basic scientific research will be immense, as many scientific projects that are now infeasible, or which require computation measured in weeks or months, will produce results within minutes or days. The facility will encourage rapid development of complex image and connectomic analysis, pattern recognition, and data mining algorithms, often working on high- dimensional multi-parametric data, thereby allowing us to maximize the amount and accuracy of information gathered from biomedical images. Data mining of large databases and of complex data will expose new relationships between genotypes and phenotypes, and will potentially reveal subtle characteristics of certain pathologies that have clinical values. It will also aid Penn's strong focus on translational research in the field of medical imaging, which is currently limited by the lack of a sufficiently powerful computer system to facilitate the demanding imaging studies. The basic and clinical research that the proposed computational server will enable is expected to have a very significant clinical impact, underlining the importance of the project.

抽象的 生物医学图像计算和信息学集群（BICIC）将满足快速增长的需求 宾夕法尼亚大学和生物医学图像计算中心的生物医学图像计算研究 特别是，以及该中心由 NIH 资助的合作研究网络。生物医学图像 计算面临着一些挑战。算法复杂性增加需要计算 对大量成像、临床和遗传数据进行密集计算和数据挖掘 为了发现具有生物学和临床重要性的疾病，需要不断增加的患者群体 关系。这些挑战凸显了对先进计算和存储设施的需求 BICIC 将提供。拟议的工具代表了大约 7 倍的增长 当前可用的资源，允许更快速地执行现有的计算机化 分析并提供探索当前不可行的方法的能力 设备。这种计算能力可在单个设施中使用，而不是在分散的资源中 各个实验室的合作将实现算法、编程方法、数据集和 目前无法实现的加工技术。拟议的服务器将提供一个平台 这些复杂且要求严格的算法的开发人员和用户突破了极限 将生物医学图像计算科学提升到新的水平。拟议的超级计算机将允许高 扫描的吞吐量分析，加速知识发现和进一步分析的设计。这 这样一个系统对基础科学研究的贡献将是巨大的，因为许多科学项目 现在不可行的，或者需要以周或月为单位进行计算的，将产生 几分钟或几天内即可得到结果。该设施将鼓励复杂图像和 连接组分析、模式识别和数据挖掘算法，通常致力于高 维度多参数数据，从而使我们能够最大限度地提高数据的数量和准确性 从生物医学图像收集的信息。大型数据库和复杂数据的数据挖掘 将揭示基因型和表型之间的新关系，并有可能揭示微妙的 具有临床价值的某些病理特征。这也将有助于宾夕法尼亚大学集中精力 医学影像领域的转化研究目前由于缺乏 足够强大的计算机系统可以促进要求严格的成像研究。基本和 所提议的计算服务器将实现的临床研究预计将具有非常好的效果 显着的临床影响，强调了该项目的重要性。

项目成果

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

使用随后的边界距离回归和像素分类网络在超声图像中自动分割肾脏。

• 发表时间: 2020
• 影响因子: 10.9
• 作者: Yin, Shi;Peng, Qinmu;Li, Hongming;Zhang, Zhengqiang;You, Xinge;Fischer, Katherine;Furth, Susan L;Tasian, Gregory E;Fan, Yong
• 通讯作者: Fan, Yong

Quantification of Thoracic Lymphatic Flow Patterns Using Dynamic Contrast-enhanced MR Lymphangiography.

使用动态对比增强 MR 淋巴管造影术量化胸腔淋巴流动模式。

• 发表时间: 2020
• 影响因子: 19.7
• 作者: Zheng, Qiang;Itkin, Maxim;Fan, Yong
• 通讯作者: Fan, Yong

Automatic Segmentation and Quantification of Upper Airway Anatomic Risk Factors for Obstructive Sleep Apnea on Unprocessed Magnetic Resonance Images.

在未处理的磁共振图像上自动分割和量化阻塞性睡眠呼吸暂停的上呼吸道解剖学危险因素。

• 发表时间: 2023-03
• 影响因子: 4.8
• 作者: Bommineni, Vikas L;Erus, Guray;Doshi, Jimit;Singh, Ashish;Keenan, Brendan T;Schwab, Richard J;Wiemken, Andrew;Davatzikos, Christos
• 通讯作者: Davatzikos, Christos

Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes.

基于神经生物学的新发抑郁症和精神病的分层：两种不同的跨诊断表型的识别。

• 发表时间: 2022-10-01
• 影响因子: 10.6
• 作者: Lalousis, Paris Alexandros;Schmaal, Lianne;Wood, Stephen J;Reniers, Renate L E P;Barnes, Nicholas M;Chisholm, Katharine;Griffiths, Sian Lowri;Stainton, Alexandra;Wen, Junhao;Hwang, Gyujoon;Davatzikos, Christos;Wenzel, Julian;Kambeitz
• 通讯作者: Kambeitz

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma.

代谢和生理磁共振成像可区分胶质母细胞瘤患者的真实进展与假性进展。

• 发表时间: 2022-07
• 影响因子: 2.9
• 作者: Chawla, Sanjeev;Bukhari, Sultan;Afridi, Omar M;Wang, Sumei;Yadav, Santosh K;Akbari, Hamed;Verma, Gaurav;Nath, Kavindra;Haris, Mohammad;Bagley, Stephen;Davatzikos, Christos;Loevner, Laurie A;Mohan, Suyash
• 通讯作者: Mohan, Suyash