Optimization- and learning-based algorithms for medical image computing

1)问题、困难和现有技术： 自动高效地在数字图像中找到有意义的区域，例如3D医学扫描中的器官或照片中的人，是计算机视觉和医学成像界的一个极其重要的研究问题，因为其理论和方法方面的挑战以及许多有用的应用。 目前的主要应用领域包括医学图像分析、机器人学、人机交互、图像检索和编辑、安全和监控、导航、制造、遥感等许多领域。例如，在医学成像中，解剖结构的3D表面的自动检测和可视化对于有效的疾病诊断/治疗/后续、手术规划、放射学报告和整个卫生保健实践是必不可少的。从技术角度来看，由于医学扫描的各种和复杂的外观，这样的检测问题是困难的，并且通常依赖于应用。文献中的方法大多基于经典的计算机视觉技术，只能处理一小部分现实世界的问题，原因如下：(I)它们没有充分利用可用的先验知识，即机器可以从人类专家产生的一组训练结构/表面学习的上下文信息。到目前为止，用来模拟这种知识的数学描述还不够复杂，不足以反映人类对医学扫描的理解；以及(Ii)在许多现实世界的场景中，它们可能非常慢。 2)目标： 该研究计划的总体目标是开发经过充分训练的、有效的(实时)和理论上可靠的算法，用于从医学扫描中自动检测和可视化各种器官的3D表面。具体目标包括： (I)理论目标：我们打算基于信息论测量和机器学习的最新进展来定义新的能量泛函。我们还打算为这类泛函设计新颖而有效的优化技术。总体目的是确定包含重要先验知识的解决方案，这些先验知识在当前算法中被省略或过度简化；以及 (Ii)实践目标：在实践中，我们计划设计我们的技术调查，以解决各种具有挑战性和重要的问题，例如寻找主动脉、脊柱、心脏、肝脏、前列腺、脑瘤以及多个腹部器官的3D结构，仅举几个例子。 3)科学方法： (I)方法论：我们的方法论基于以下主要步骤：(A)使用人类专家建立的训练数据，建立描述感兴趣结构(例如，形状、几个器官之间的几何关系和高级医学知识)的上下文知识的复杂泛函；(B)对新泛函的最小化进行数学研究和数值求解；以及(D)通过与人类专家建立的地面真实数据进行比较，对算法进行实验评估。 (2)技术和理论上的新颖性/重要性：我们预计，我们的提法会导致具有挑战性的优化问题，而这些问题不能用标准技术直接解决。我们打算得到原始的近似或界限，从而设计出计算机视觉和医学成像中仍未使用的新颖而有效的优化技术。我们将重点介绍最优化转移和凸松弛方法。我们预计，我们打算开发的解决方案将：(A)适用于广泛的问题；(Ii)在精度和速度方面产生最先进的性能。

1) Problems, difficulties and prior art: Finding automatically and efficiently meaningful regions in a numerical image, for instance an organ in a 3D medical scan or a person in a photograph, is a research problem of paramount importance within the computer vision and medical imaging communities for its theoretical and methodological challenges, and numerous useful applications. Current major application areas include medical image analysis, robotics, human-computer interaction, image retrieval and editing, security and surveillance, navigation, manufacturing, remote sensing and many others. For instance, in medical imaging, automatic detection and visualization of the 3D surfaces of anatomic structures is essential to efficient disease diagnosis/treatment/follow-up, surgery planning, radiologic reporting and health care practices at large. From a technical point of view, such detection problems are difficult and often application-dependent because of the various and complex appearances of medical scans. Mostly based on classical compute-vision techniques, the methods in the literature can handle only a small fraction of real-world problems because of the following reasons: (i) they do not take full advantage of the available prior knowledge, i.e., the contextual information that a machine can learn from a set of training structures/surfaces produced by a human expert. The mathematical descriptions used so far to model such knowledge are not complex enough to reflect human understanding of medical scans; and (ii) they may be very slow in many real-world scenarios. 2) Objectives: The overall objective of this research program is to develop fully trained, efficient (real-time) and theoretically sound algorithms for the automatic detection and visualization of the 3D surfaces of various organs from medical scans. Specific objectives include: (i) Theoretical objectives: We intend to define novel energy functionals based on information-theoretic measures and recent advances in machine learning. We further intend to design original and efficient optimization techniques for such functionals. The overall purpose is to determine solutions which embody important prior knowledge that has been either omitted or oversimplified in current algorithms; and (ii) Practical objectives: In practice, we plan to devise our technical investigations to solving various challenging and important problems, e.g., finding the 3D structures of the aorta, spine, heart, liver, prostate, brain tumors, as well as multiple abdominal organs, just to name a few examples. 3) Scientific approach: (i) Methodology: Our methodology is based on the following main steps: (a) building sophisticated functionals that describe contextual knowledge about the structures of interest (e.g., shape, geometric relations between several organs and high-level medical knowledge) using training data built by human experts; (b) investigating mathematically and solving numerically the minimization of the new functionals; and (d) evaluating experimentally the algorithms by comparisons to ground-truth data built by human experts. (ii) Technical and theoretical novelty/significance: We anticipate that our formulations lead to challenging optimization problems, which cannot be solved directly with standard techniques. We intend to derive original approximations or bounds, thereby designing novel and efficient optimization techniques that are still not in use in computer vision and medical imaging. We intend to focus on optimization-transfer and convex relaxation approaches. We anticipate the solutions we intend to develop will (a) be applicable to a breadth of problems; and (ii) yield state-of-the-art performances in regard to accuracy and speed.