Optimization- and learning-based algorithms for medical image computing

1)问题，困难和现有技术：在数字图像中自动有效地找到有意义的区域，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社区中最重要的研究问题，因为它的理论和方法挑战，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、人机交互、图像检索和编辑、安全与监控、导航、制造、遥感等。例如，在医学成像中，解剖结构三维表面的自动检测和可视化对于有效的疾病诊断/治疗/随访、手术计划、放射报告和医疗保健实践至关重要。从技术角度来看，由于医学扫描的各种复杂外观，这种检测问题很困难，而且往往依赖于应用。文献中的方法大多基于经典的计算机视觉技术，由于以下原因，它们只能处理一小部分现实世界的问题：(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/表面中学习的上下文信息。迄今为止用于模拟此类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；（ii）在许多现实场景中，它们可能非常缓慢。2)目标：本研究项目的总体目标是开发训练有素、高效（实时）且理论上合理的算法，用于医学扫描中各种器官三维表面的自动检测和可视化。具体目标包括：(i)理论目标：我们打算基于信息论措施和机器学习的最新进展定义新的能量函数。我们进一步打算为这些功能设计原创和有效的优化技术。总体目的是确定包含重要先验知识的解决方案，这些知识在当前算法中被忽略或过度简化；（ii）实践目标：在实践中，我们计划设计我们的技术研究来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，脑肿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：我们的方法基于以下主要步骤：(a)使用人类专家构建的训练数据构建复杂的函数，描述有关感兴趣结构的上下文知识（例如，形状，几个器官之间的几何关系和高级医学知识）；(b)对新泛函的最小化问题进行数学研究和数值求解；(d)通过与人类专家建立的真实数据进行比较，对算法进行实验评估。（ii）技术和理论的新颖性/重要性：我们预计我们的配方会导致具有挑战性的优化问题，这些问题无法用标准技术直接解决。我们打算推导出原始的近似或边界，从而设计出新颖有效的优化技术，这些技术尚未在计算机视觉和医学成像中使用。我们打算把重点放在优化转移和凸松弛方法上。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；并且（ii）在准确性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.