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

医学图像分割是将医学图像分割成器官、肿瘤等有意义的对象的过程，是医学专业人员为患者提供定制医疗服务的关键工具。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。在过去的十年里，生物医学成像取得了巨大的技术进步，导致了大量新的和改进的医疗数据，这就产生了对能够更快、更彻底地处理这些数据的算法的需求。研究人员已经进行了广泛的工作来开发这些医学图像分割算法，但目前的算法存在以下缺点：1)它们不具备有效表示各种各样医疗对象的不同形状的能力和/或2)它们需要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，可以应用于各种类型的医学图像，并且可以由任何具有基本计算机知识的用户执行。许多重要的物体将能够用同样的算法处理，比如肝脏、前列腺和椎骨。这项研究允许医学专家花更少的时间分析各种各样的医学图像，更多的时间直接与患者一起工作。该算法将适用于任何感兴趣的医学成像对象，其形状可以分解为具有非常简单几何结构的少量组件。例如，每个人的肝脏可能略有不同，但几乎所有的肝脏都可以表示为两个或三个“星形”成分的结合。如果组件中有一个中心点，使得连接该中心和组件中每个其他点的线段包含在该对象中，则该组件被定义为星形。如果已知单个星形部件的中心，则可以通过计算机算法非常快速地识别整个部件，但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、椎骨等多种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物体所有分量的算法。结果将是一个单一的算法，该算法将应用于许多场景，并且可以由非技术用户执行。

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.