Automated Alignment and Segmentation for Electron Tomography

电子断层扫描的自动对准和分割

• 批准号: 0241182
• 负责人: Qiang Ji
• 金额: --
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241182&HistoricalAwards=false
• 关键词: Automated; Alignment; Segmentation; Electron; Tomography

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.

0241182JI当前项目解决了两个程序瓶颈，这些程序瓶颈限制了高通量电子断层扫描：图像对齐和体积分割。目前，图像对齐需要使用基准标记，并且相对劳动密集型和容易发生。提出了一个信息理论过程，该程序执行没有基准标记的自动图像对齐。提出的解决方案利用了层析成像图像中的丰富信息内容，并明确说明了图像之间的唯一转换。体积分割是电子断层扫描中最关键，劳动量，耗时和主观步骤的最关键，但劳动力较高的阶段之一。肾小管对象的自动分割可能非常具有挑战性，因为相对于周围环境的对象极为低，表面拓扑高度不平衡和不规则的表面拓扑以及细胞附着的显着变化。提出的分割方法包括将问题分为三个主要任务：1）管状结构增强； 2）管状结构检测； 3）管状表面形态重建和终端结构的检测。在第一个任务中，提出了一个基于模型的过滤器，该滤波器将增强圆柱结构，同时逐渐强调无关的结构。在第二任任务中，提出了一种可靠的特征检测技术来定位纤维的管状部分。在第三任任务中，提出了一种统计的局部生长技术，该技术将在各个方向上生长检测到的基础圆柱体，以产生实际结构的表面形态和内部不连续性。 所提出的方法将广泛适用于电子断层扫描和其他形式的医学成像。对齐方法是完全一般的，并且在细胞和医学成像中的微管以及类似的管状或纤维基序的流行率可确保分割方法的广泛适用性。通过适当的修改，建议的分割方法可以适应膜和囊泡几何形状。通过解决两个瓶颈步骤，所提出的方法有可能将电子断层扫描转换为一个强大的常规工具进行研究和诊断研究。这些方法还可能应用于其他医学成像项目。因此，该项目旨在使电子断层扫描能够实现其在后基因组时代分析亚细胞结构和功能的全部潜力。该赠款是根据DMS/NIGMS联合倡议提供的，以支持数学生物学领域的研究补助金。这是由国家科学基金会的数学科学系（DMS）和美国国立卫生研究院的国家一般医学科学研究所（NIGMS）赞助的。