Learning to Register, Segment and Retrieve Medical Images

放射线医师等专家对医用图像的分析对于癌症等多种疾病的诊断和治疗都是重要的任务。经常使用软件程序来协助专家。医学图像分析的三个主要软件操作是配准（对齐不同图像中的解剖结构），分割（标记器官或肿瘤的边界）和检索（在大型数据库中查找图像）。医学图像的配准、分割和检索不仅是一个非常具有挑战性的问题（准确性和效率都是非常困难的），而且它们是相互关联的。例如，配准可以用于分割，并且分割可以用于图像检索。此外，在大多数情况下，当使用其中一种方法时，通常也需要其他两种方法。现有的技术忽略了这一关系，将配准、分割和检索问题分开解决。一个统一的解决方案，考虑到这些技术之间的关系，预计将提供更准确的结果和节省资源。本研究的目的是探讨多个问题。首先，我们将更仔细地研究这些任务在医学图像分析中的关系，其次，我们将开发一个统一的框架，可以通过图像的特征来学习这些任务。在这项研究中，几个学习计划，如不断发展的规则和加强的权重，将被检查，以设计一个新的类的算法，这三个任务。我们将尝试为所有三种操作定义一个统一的框架，通过学习模态和解剖规范，可以自主调整自身以适应手头的任务。为此，图像将被表征为具有局部和全局特征以及编码解剖结构。用于配准、分割和检索医学图像的统一框架将提高临床工作流程的效率，并通过结合专家反馈来帮助建立高质量的结果。这将有利于临床医生（减少繁琐的任务），医院（节省时间和资源）和患者（高质量的护理）。

Analysis of medical images by an expert such as a radiologist is an important task for both diagnosis and treatment of many diseases such as cancer. Software programs are regularly used to assist the experts. Three main software operations of medical image analysis are registration (aligning anatomical structures in different images), segmentation (marking the boundaries of an organ or tumour) and retrieval (finding an image in a large database). Registering, segmenting and retrieving medical images are not only very challenging problems (achieving accuracy and efficiency is very difficult for all them), but also they are interrelated. For instance, registration can be used in segmentation, and segmentation can be used for image retrieval. Besides, in most cases, when one of these methods is used, the other two are generally required too. The current technologies neglect this relationship and spend a lot of effort to solve the problems of registration, segmentation and retrieval separately. A unified solution, that considers the relationship between these techniques, is expected to provide more accurate results and save resources. The purpose of this research is to investigate multiple questions. Firstly, we will examine the relationship of theses tasks in medical image analysis more closely, and secondly, we will develop a unified framework that can learn these tasks by characterization of images. In this research, several learning schemes, such as evolving rules and reinforced weights, will be examined to design a new class of algorithms for these three tasks. We will attempt to define a unified framework for all three operations that can autonomously adjust itself to the task at hand by learning from the modality and anatomy specifications. For this purpose, images will be characterized with local and global features as well as with encoding anatomical structures. A unified framework for registering, segmenting and retrieving medical images will increase the efficiency of clinical workflow and help to establish high quality results by incorporating expert feedback. This will have benefits for clinicians (less tedious tasks), hospitals (saving time and resources) and patients (high-quality care).