Automated Alignment and Segmentation for Electron Tomography

0241182Ji本项目解决了限制高通量电子断层扫描的两个程序瓶颈：图像对齐和体积分割。目前，图像对齐需要使用基准标记，并且相对费力且容易出错。提出了一种无基准标记自动图像配准的信息论方法。该方法充分利用了层析图像中丰富的信息内容，显式地解决了图像之间的唯一变换问题。体积分割是电子断层扫描中最关键的步骤之一，但也是劳动密集型、耗时和主观的步骤。管状物体的自动分割可能是非常具有挑战性的，因为物体相对于周围环境的对比度极低，表面拓扑高度不均匀和不规则，以及细胞附着的显著变化。该分割方法将问题分解为三个主要任务：1)管状结构增强；2)管状结构检测；3)管状表面形态重建和端部结构检测。对于第一个任务，提出了一种基于模型的过滤器，它将增强圆柱形结构，而不强调无关的结构。对于第二个任务，提出了一种稳健的特征检测技术来定位纤维的管状部分。对于第三个任务，提出了一种统计局部区域生长技术，该技术将在所有方向上生长被检测到的下层圆柱体，以产生实际结构的表面形态和内部不连续。所提出的方法将广泛适用于电子断层成像和其他形式的医学成像。对齐方法是完全通用的，细胞和医学成像中普遍存在的微管和类似的管状或纤维图案确保了分割方法的广泛适用性。经过适当的修改，所提出的分割方法可以适用于膜和囊泡的几何形状。通过解决这两个瓶颈步骤，建议的方法有可能将电子断层扫描转变为一种强大的、常规的研究和诊断调查工具。这些方法对其他医学影像项目也有潜在的应用价值。因此，该项目旨在使电子断层扫描能够充分发挥其在后基因组时代分析亚细胞结构和功能的潜力。这笔赠款是在DMS/NIGMS联合倡议下提供的，以支持数学生物学领域的研究资助。这是一项由国家科学基金会数学科学部(DMS)和国立卫生研究院(NIGMS)国家普通医学研究所(NIGMS)主办的联合竞赛。

0241182Ji The current project addresses two procedural bottlenecks that limit high throughput electron tomography: image alignment and volume segmentation. Presently, image alignment requires the use of fiducial markers and is relatively labor-intensive and error prone. An information theoretic procedure is proposed that performs automatic image alignment without fiducial marks. The proposed solution exploits the rich information content in tomographic images and explicitly accounts for the unique transformation between images. Volume segmentation is one of the most crucial, yet labor-intensive, time-consuming, and subjective steps in electron tomography. Automated segmentation of tubular objects can be very challenging because of extremely low object contrast relative to the surroundings, a highly uneven and irregular surface topology, and significant variations in cellular attachments. The proposed segmentation approach consists of dividing the problem into three major tasks: 1) tubular structure enhancement; 2) tubular structure detection; and 3) tubular surface morphology reconstruction and detection of end structures. Towards the first task, a model-based filter is proposed that will enhance cylindrical structures while de-emphasizing the irrelevant structures. Towards the second task, a robust feature detection technique is proposed to localize the tubular portion of fibers. Towards the third task, a statistical local region growing technique is proposed that will grow the detected underlying cylinder in all directions to produce the surface morphology and internal discontinuities of the actual structure. The proposed methods will have wide applicability to electron tomography and other forms of medical imaging. The alignment methods are completely general and the prevalence of microtubules and similar tubular or fibrous motifs in cellular and medical imaging ensures wide applicability of the segmentation methods. With appropriate modifications, the proposed segmentation methods could be adapted to membrane and vesicle geometries. By addressing the two bottleneck steps, the proposed methods have the potential to transform electron tomography into a powerful, routine tool for research and diagnostic investigations. These methods also have potential application to other medical imaging projects. Thus, this project is designed to enable electron tomography to realize its full potential for analysis of subcellular structure and function in the post-genomic era. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.