AitF: Collaborative Research: Automated Medical Image Segmentation via Object Decomposition

AitF：协作研究：通过对象分解进行自动医学图像分割

• 批准号: 1733742
• 负责人: Xiaodong Wu
• 金额: $ 39.23万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733742&HistoricalAwards=false
• 关键词: AitF; Collaborative; Research; Automated; Medical

Medical image segmentation, the process of dividing a medical image into meaningful objects such as organs, tumors, etc., is a critical tool that allows medical professionals to provide customized medical care to patients. In the past this highly technical, individualized care has required experts to manually analyze the images, a process that is very expensive in both time and money. Over the past decade, enormous technological advances have been made in biomedical imaging, leading to a large amount of new and improved medical data which has created a demand for algorithms which can process this data faster and more thoroughly. Researchers have worked extensively to develop these medical image segmentation algorithms, but current algorithms suffer from the following drawbacks: 1) they do not have the capability of effectively representing diverse shapes of a wide variety of medical objects and/or 2) they require substantial interaction from an expert user. This research will develop a novel medical image segmentation algorithm that can be applied to various types of medical images and will be able to be executed by any user with basic computer literacy. Many important objects will be able to be handled with the same algorithm, such as livers, prostates, and vertebrae. This research allows medical experts to spend less time analyzing a wide variety of medical images and more time directly working with patients. The algorithm will work for any medical imaging object of interest whose shape can be decomposed into a small number of components with a very simple geometric structure. For example, livers may be slightly different from person to person, but almost all livers can be represented as a union of two or three "star-shaped" components. A component is defined to be star-shaped if there is a center point in the component such that the line segment connecting the center to every other point in the component is contained within the object. If the center of a single star-shaped component is known, then the whole component can be very quickly identified by computer algorithms, but as the number of components increases, the simultaneous computation of all the components becomes much more difficult. This research will develop algorithms which can automatically compute the centers of the star-shaped components for many medical imaging objects such as livers, prostates, and vertebrae, and further will develop algorithms that can simultaneously identify all the components for the objects. The result will be a single algorithm that will be applied to many scenarios and can be executed by non-technical users.

医学图像分割，将医学图像分割成有意义的对象（如器官、肿瘤等）的过程，是一个重要的工具，允许医疗专业人员为患者提供定制的医疗护理。 在过去，这种高度技术性的个性化护理需要专家手动分析图像，这一过程在时间和金钱上都非常昂贵。 在过去的十年中，生物医学成像取得了巨大的技术进步，产生了大量新的和改进的医疗数据，这就需要能够更快，更彻底地处理这些数据的算法。 研究人员已经广泛地工作来开发这些医学图像分割算法，但是当前的算法具有以下缺点：1）它们不具有有效地表示各种各样的医学对象的不同形状的能力和/或2）它们需要来自专家用户的大量交互。 本研究将开发一种新的医学图像分割算法，可应用于各种类型的医学图像，并将能够由任何具有基本计算机素养的用户执行。 许多重要的对象将能够用相同的算法处理，如肝脏、前列腺和椎骨。 这项研究使医学专家能够花更少的时间分析各种各样的医学图像，并有更多的时间直接与患者合作。该算法将适用于任何感兴趣的医学成像对象，其形状可以被分解成具有非常简单的几何结构的少量组件。 例如，肝脏可能因人而异，但几乎所有的肝脏都可以表示为两个或三个“星形”组件的联合。 如果元件中有一个中心点，使得将该中心连接到元件中每个其他点的线段包含在对象内，则该元件被定义为星形。 如果已知单个星状部件的中心，那么整个部件可以通过计算机算法非常快速地识别，但是随着部件数量的增加，同时计算所有部件变得更加困难。 本研究将开发能够自动计算肝脏、前列腺和椎骨等许多医学成像对象的星形分量中心的算法，并进一步开发能够同时识别对象的所有分量的算法。 结果将是一个单一的算法，将应用于许多场景，并可以由非技术用户执行。

项目成果

Multi-scale segmentation using deep graph cuts: Robust lung tumor delineation in MVCBCT

• DOI: 10.1109/isbi.2018.8363628
• 发表时间: 2018-04
• 期刊: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
• 作者: Xiaodong Wu;Zisha Zhong;J. Buatti;Junjie Bai

Automated macular OCT retinal surface segmentation in cases of severe glaucoma using deep learning

使用深度学习对严重青光眼病例进行自动黄斑 OCT 视网膜表面分割

• DOI: 10.1117/12.2611859
• 发表时间: 2022
• 期刊: Medical Imaging 2022: Image Processing
• 作者: Xie, Hui;Wang, Jui-Kai;Kardon, Randy H.;Garvin, Mona K.;Wu, Xiaodong
• 通讯作者: Wu, Xiaodong

KCB-Net: A 3D knee cartilage and bone segmentation network via sparse annotation.

• DOI: 10.1016/j.media.2022.102574
• 发表时间: 2022-11
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Peng, Yaopeng;Zheng, Hao;Liang, Peixian;Zhang, Lichun;Zaman, Fahim;Wu, Xiaodong;Sonka, Milan;Chen, Danny Z.
• 通讯作者: Chen, Danny Z.

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images

• DOI: 10.1364/boe.9.004509
• 发表时间: 2018-09-01
• 期刊: BIOMEDICAL OPTICS EXPRESS
• 影响因子: 3.4
• 作者: Shah, Abhay;Zhou, Leixin;Wu, Xiaodong
• 通讯作者: Wu, Xiaodong

Deep segmentation networks predict survival of non-small cell lung cancer

• DOI: 10.1038/s41598-019-53461-2
• 发表时间: 2019-11-21
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Baek, Stephen;He, Yusen;Wu, Xiaodong
• 通讯作者: Wu, Xiaodong