Rigid motion steerability for multiscale stochastic models of 3D-textures applied to soft tissue segmentation/identification in 3D-biomedical images

现代医学和生物学从成像技术的进步中获益匪浅。新的设备和获取方法使第一批病毒图像成为可能。诊断成像的分辨率现在在几百微米左右，而且是3D的；例如核磁共振或CT扫描。尽管有这些进步，但医学图像中的一些信息是潜在的，提取这些信息往往是一项繁琐的任务。实现更精细的分辨率水平并不能自动使每个组织对医生的眼睛可见。成像边界的扩大不仅大大增加了可用数据的量，而且使人们想要从图像中提取更多的信息。因此，对可靠、自动化或半自动化图像分析工具的开发需求不断增长。考虑到这一目标，这个项目的跨学科研究小组旨在做出理论和算法上的贡献，从而导致这些工具的发展。推动这个项目的问题是如何在3d图像中以高空间精度识别或分割医学从业者或生物学家感兴趣的软组织。对我们不利的是，大多数时候诊断感兴趣的组织具有很大的可变性，体积小，对比度低，并且被非标准噪声破坏。基于软组织与3d纹理相关联的前提，研究人员将软组织鉴别/识别视为对感兴趣组织产生的3d纹理的分割/识别。我们已经在2D中努力解决了这个问题，但在3D中却几乎没有触及到它。为了在三维纹理的分割/识别中达到较高的空间精度，研究人员将建立新的三维刚性运动不变纹理特征的概率模型。这将减少甚至可能消除由于组织在3d空间中的定位而导致的分类错误。为了提取这样的签名，我们将表征并彻底研究相对于3d刚性运动的协变（可操纵）的多尺度数据表示。该项目的一个主要挑战是提取具有合理长度的3d刚性运动不变纹理特征，并采用概率模型以计算可管理的方式管理这些特征的分类。设想的工具将在（3D） ct血管造影扫描和锥体神经元的3D共聚焦显微镜图像中进行测试。在第一种情况下，我们希望分割各种软组织，如心肌、心外膜脂肪、管腔和钙，而在第二种情况下，我们希望在嘈杂的背景下识别树突。研究人员的目标是开发一种基于新颖3d数据表示的软组织分割算法平台，而不是定制应用程序。这项研究计划要求在数学分析和概率论方面发展新的数学思想。这些新的数学概念和方法将赋予设想的算法一种独特的能力，这种能力是人类视觉所固有的，但在计算机和机器人视觉中尚未实现：独立于它们在3d空间中的位置来识别结构和模式。事实上，组织必须被任何自动图像分析系统正确识别，无论它们在3d空间或人体中的位置如何。具有这种能力的系统将能够在3d空间的每个方向上以相同的高精度划定组织边界。该算法平台可用于医学和生物学领域的各种成像应用，如用于诊断冠状动脉狭窄的CT血管造影或用于检测肝癌的对比CT。检测冠状动脉壁的异常，尤其是升主动脉近端区域的异常，将有助于预防最危及生命的梗死，并可能监测动脉粥样硬化斑块的治疗，而无需频繁使用严重侵入性血管内超声探头。在肝脏癌变发展的早期阶段识别出癌变可以显著增加这种类型癌症的生存机会。在3d共聚焦显微镜获得的图像中，准确捕捉树突的结构及其突出的附着物棘的结构是一个重要的目标，因为棘似乎是理解抑郁症和双相情感障碍的生物学基础的关键。

Modern medicine and biology have been enormously benefited from the advancement of imaging. New devices and acquisition methods enabled the first images of viruses. Resolution levels for diagnostic imaging are now at the order of a few hundred microns and in 3D; e.g. MRI or CT scans. Despite of all these advances some information in medical images is latent and extracting it is often a tedious task. Achieving finer resolution levels does not automatically make every tissue visible to the eye of the practitioner. The expansion of the imaging frontiers not only increases grossly the volume of the available data but also makes to want to extract more information from an image. Thus, there is an ever growing demand for the development of reliable, automated or semi-automated image analysis tools. With this goal in mind the interdisciplinary group of investigators in this project aims in making theoretical and algorithmic contributions that can lead to the development of such tools.The problem motivating this project is how to identify or segment soft-tissues that are of interest to medical practitioners or biologists with high spatial accuracy in 3D-images. To our detriment, most of the time tissues of diagnostic interest have great variability, small volume, low contrast and are corrupted by non-standard noise. Based on the premise that soft-tissues are associated with 3D-textures, the investigators approach soft-tissue discrimination/identification as segmentation/identification of the 3D-textures resulting from the tissues of interest. Notable efforts have been made to solve this problem in 2D but in 3D it is practically untouched. To achieve high spatial accuracy in the segmentation/identification of 3D-textures the investigators will build novel probabilistic models for 3D-rigid motion invariant texture signatures. This will reduce or may even eliminate classification errors due to the positioning of a tissue in the 3D-space. To extract such signatures we will characterize and thoroughly study multiscale data representations that are covariant (steerable) with respect to 3D-rigid motions. A major challenge of this project is to extract 3D-rigid motion invariant texture signatures with reasonable length and adopt probabilistic models governing the classification of these signatures in a computationally manageable manner. The envisioned tools will be tested in (3D) CT-angiography scans and 3D-confocal microscopy images of pyramidal neurons. In the first case we wish to segment various soft tissues such as cardiac muscle, epicardial fat, lumen and calcium while in the second we wish to identify dendrites in a noisy background. The investigators aim in developing an algorithmic platform for soft-tissue segmentation based on novel 3D-data representations rather than a customized application. This research program requires the development of novel mathematical ideas both in mathematical analysis and in probability theory. These new mathematical concepts and methods will endow the envisioned algorithms with a unique ability native to human vision but not yet achieved in computer and robotic vision: the identification of structures and patterns independently of their position in the 3D-space. Indeed, tissues must be correctly identifiable by any automated image analysis system regardless of their position in the 3D-space or in the human body. A system with this ability will be able to circumscribe tissue boundaries with the same high accuracy in every direction in the 3D-space. This algorithmic platform can be adopted for a wide variety of imaging applications in medicine and biology, such as CT-angiography used to diagnose stenosis in coronary arteries or contrast CT for the detection of liver cancer. Detecting abnormalities in the walls of coronary arteries especially of their regions proximal to the ascending aorta will help prevent the most life-threatening infarctions and possibly monitor the treatment of the atherosclerotic plaque without the frequent use of the grossly invasive intravascular ultrasound probes. Identifying cancerous lesions in the liver at their early stages of development can significantly increase the chances of survival in this type of cancer. Capturing accurately the structure of dendrites and of their protruding attachments called spines in images acquired with 3D-confocal microscopes is a prime time goal as spines seem to hold the key of understanding the biological basis of depression and bipolar disorder.