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.

摘要 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 尤其是该中心由美国国立卫生研究院资助的合作研究网络。生物医学影像 计算面临着几个挑战。增加的算法复杂性要求在计算上 对大量成像、临床和遗传数据进行密集的计算和数据挖掘 为了发现生物学和临床上的重要意义，需要不断增加的患者群体。 两性关系。这些挑战突显了对先进计算和存储设施的需求 BICIC将提供的。拟议的工具比起以前增加了大约7倍 目前可用的资源，既可以更快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。在单个设施中获得这种计算能力，而不是分散的资源 将在算法、编程方法、数据集和 目前还不可能的处理技术。拟议的服务器将为以下方面提供平台 这些复杂且要求苛刻的算法的开发人员和用户要突破 生物医学图像计算科学迈上新台阶。拟议中的超级计算机将允许高 扫描吞吐量分析，加速知识发现和进一步分析的设计。这个 这种系统对基础科学研究的贡献将是巨大的，就像许多科学项目一样 现在是不可行的，或者需要以周或月为单位进行计算的，将产生 在几分钟或几天内得出结果。该设施将鼓励快速开发复杂的图像和 连通分析、模式识别和数据挖掘算法，通常致力于 多维多参数数据，从而使我们能够最大限度地提高 从生物医学图像中收集的信息。大型数据库和复杂数据的数据挖掘 将揭示基因类型和表型之间的新关系，并可能揭示微妙的 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将重点放在 医学成像领域的转化性研究，目前受到缺乏 足够强大的计算机系统，以方便要求苛刻的成像研究。基本的和 拟议的计算服务器将实现的临床研究预计将有非常 显著的临床影响，强调了该项目的重要性。

项目成果

The Liver Tumor Segmentation Benchmark (LiTS).

• DOI: 10.1016/j.media.2022.102680
• 发表时间: 2023-02
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Bilic, Patrick;Christ, Patrick;Li, Hongwei Bran;Vorontsov, Eugene;Ben-Cohen, Avi;Kaissis, Georgios;Szeskin, Adi;Jacobs, Colin;Mamani, Gabriel Efrain Humpire;Chartrand, Gabriel;Lohoefer, Fabian;Holch, Julian Walter;Sommer, Wieland;Hofmann, Felix;Hostettler, Alexandre;Lev-Cohain, Naama;Drozdzal, Michal;Amitai, Michal Marianne;Vivanti, Refael;Sosna, Jacob;Ezhov, Ivan;Sekuboyina, Anjany;Navarro, Fernando;Kofler, Florian;Paetzold, Johannes C.;Shit, Suprosanna;Hu, Xiaobin;Lipkova, Jana;Rempfler, Markus;Piraud, Marie;Kirschke, Jan;Wiestler, Benedikt;Zhang, Zhiheng;Huelsemeyer, Christian;Beetz, Marcel;Ettlinger, Florian;Antonelli, Michela;Bae, Woong;Bellver, Miriam;Bi, Lei;Chen, Hao;Chlebus, Grzegorz;Dam, Erik B.;Dou, Qi;Fu, Chi-Wing;Georgescu, Bogdan;Giro-I-Nieto, Xavier;Gruen, Felix;Han, Xu;Heng, Pheng-Ann;Hesser, Jurgen;Moltz, Jan Hendrik;Igel, Christian;Isensee, Fabian;Jaeger, Paul;Jia, Fucang;Kaluva, Krishna Chaitanya;Khened, Mahendra;Kim, Ildoo;Kim, Jae-Hun;Kim, Sungwoong;Kohl, Simon;Konopczynski, Tomasz;Kori, Avinash;Krishnamurthi, Ganapathy;Li, Fan;Li, Hongchao;Li, Junbo;Li, Xiaomeng;Lowengrub, John;Ma, Jun;Maier-Hein, Klaus;Maninis, Kevis-Kokitsi;Meine, Hans;Merhof, Dorit;Pai, Akshay;Perslev, Mathias;Petersen, Jens;Pont-Tuset, Jordi;Qi, Jin;Qi, Xiaojuan;Rippel, Oliver;Roth, Karsten;Sarasua, Ignacio;Schenk, Andrea;Shen, Zengming;Torres, Jordi;Wachinger, Christian;Wang, Chunliang;Weninger, Leon;Wu, Jianrong;Xu, Daguang;Yang, Xiaoping;Yu, Simon Chun-Ho;Yuan, Yading;Yue, Miao;Zhang, Liping;Cardoso, Jorge;Bakas, Spyridon;Braren, Rickmer;Heinemann, Volker;Pal, Christopher;Tang, An;Kadoury, Samuel;Soler, Luc;van Ginneken, Bram;Greenspan, Hayit;Joskowicz, Leo;Menze, Bjoern
• 通讯作者: Menze, Bjoern

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

• DOI: 10.1016/j.biopsych.2022.03.021
• 发表时间: 2022-10-01
• 期刊: Biological psychiatry
• 影响因子: 10.6

Radiomics and radiogenomics in pediatric neuro-oncology: A review.

• DOI: 10.1093/noajnl/vdac083
• 发表时间: 2022-01
• 期刊: Neuro-oncology advances

HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.

• DOI: 10.1093/bioinformatics/btab193
• 发表时间: 2021-09-29
• 期刊: Bioinformatics (Oxford, England)

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.

• DOI: 10.1016/j.neo.2022.100869
• 发表时间: 2023-02
• 期刊: Neoplasia (New York, N.Y.)
• 作者: Haldar D;Kazerooni AF;Arif S;Familiar A;Madhogarhia R;Khalili N;Bagheri S;Anderson H;Shaikh IS;Mahtabfar A;Kim MC;Tu W;Ware J;Vossough A;Davatzikos C;Storm PB;Resnick A;Nabavizadeh A
• 通讯作者: Nabavizadeh A