Intelligent and Automatic Image Segmentation Software for High ThroughputAnalysi

用于高通量分析的智能自动图像分割软件

• 批准号: 8699686
• 负责人: Karyn A Esser
• 金额: $ 14.28万
• 依托单位: CYTOINFORMATICS, LLC
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8699686
• 关键词: Adipose tissue; Aging; Agreement; Algorithms; Appearance; Area; Artificial Intelligence; Basic Science; Biological; Biology; Biometry; Biopsy Specimen; Cell Nucleus; Cellular Infiltration; Characteristics; Chronic Disease; Clinical Research; Communities; Computational Science; Computer Assisted; Computer software; Data; Data Analyses; Defect; Detection; Drug Industry; E-learning; Eosine Yellowish; Exhibits; Fiber; Fibrosis; Freezing; Future; Goals; Health; Heart failure; Hematoxylin; High Performance Computing; Histology; Human; Human Pathology; Image; Image Analysis; Interobserver Variability; Intervention; Learning; Machine Learning; Malignant Neoplasms; Manuals; Marketing; Methods; Modeling; Morphologic artifacts; Muscle; Muscle Fibers; Muscle function; NIH Program Announcements; Performance; Phase; Pilot Projects; Positioning Attribute; Process; Property; Research; Research Personnel; Resources; Sampling; Scanning; Science; Scientist; Seeds; Shapes; Site; Skeletal Muscle; Slide; Small Business Technology Transfer Research; Specimen; Speed; Staging; Staining method; Stains; Techniques; Technology; Time; Tissues; United States National Institutes of Health; Update; Yang; base; bioimaging; biomedical informatics; cohort; computer science; data acquisition; design; fluorescence imaging; imaging Segmentation; improved; innovation; muscle form; muscle strength; novel; public health relevance

DESCRIPTION (provided by applicant): It is well established that aging and many chronic diseases, such as cancer and heart failure, are associated with significant losses in skeletal muscle mass and strength in humans. There is agreement across the muscle biology community that important morphological characteristics of muscle fibers, such as fiber area, the number and position of myonuclei, cellular infiltration and fibrosis are critical factors that determine the health and function of the muscle. However, at this time, quantification of muscle characteristics from standard histological and immunohistological techniques is still a manual or, at best, a semi-automatic process. This process is labor intensive and can be prone to errors, leading to high inter-observer variability. On the other hand, when muscle characteristics are calculated by computer-aided image analysis, data acquisition times decrease and objectivity improves significantly. The objective of this Phase I STTR project is to build a fully automatic, intelligent, and high throughput image acquisition and analysis software for quantitative muscle morphological analysis on digitized muscle cross-sections. We propose to utilize the most recent technical advances in machine learning and biomedical image analysis. This includes a newly developed deformable model and mean-shift based seed detection algorithm for better segmentation accuracy; an asymmetric online boosting based machine learning algorithm which allows the software to learn from errors and adjust its segmentation strategies adaptively; and a data parallelization schema using the graphic processing unit (GPU) to handle the computational bottleneck for extremely large scale image, such as whole slide scanned specimens. We believe that this software, equipped with the most advanced technical innovations, will be commercially attractive for the skeletal muscle research community including basic scientists, clinician scientists, and the pharmaceutical industry. The specific aim are: 1) Develop, implement, and validate an automatic biological image analysis software package for skeletal muscle tissue; 2) Develop a novel online updated intelligent artificial intelligence unit to enable the software to learn from errors; 3) Build a novel high performance computing unit to enable fast and high throughput automatic image analysis, which is capable of processing whole slide scanned muscle specimens. The analysis approach proposed will provide more consistent, accurate, and objective quantification of skeletal muscle morphological properties and the time for data analysis will be reduced by over a factor of 100 for standalone version and 2000 for parallel version. The long-term goal of Cytoinformatics, LLC for the Phase II stage is to apply the software to analyze histology/pathology from human muscle biopsy samples and extension of the software to other biological tissues, such as adipose tissue.

描述（由申请人提供）：众所周知，衰老和许多慢性疾病（如癌症和心力衰竭）与人类骨骼肌质量和强度的显著损失有关。肌肉生物学界一致认为，肌纤维的重要形态特征，如纤维面积、肌核的数量和位置、细胞浸润和纤维化是决定肌肉健康和功能的关键因素。然而，在这个时候，从标准的组织学和免疫组织学技术的肌肉特性的量化仍然是一个手动或，充其量，半自动的过程。这个过程是劳动密集型的，并且可能容易出错，导致观察者之间的高度可变性。另一方面，当通过计算机辅助图像分析计算肌肉特征时，数据采集时间减少，客观性显著提高。该一期STTR项目的目标是建立一个全自动，智能和高通量的图像采集和分析软件，用于数字化肌肉横截面上的定量肌肉形态分析。我们建议利用机器学习和生物医学图像分析的最新技术进展。其中包括新开发的可变形模型和基于均值漂移的种子检测算法，以提高分割准确性;基于非对称在线增强的机器学习算法，允许软件从错误中学习并自适应调整其分割策略;以及一种利用图形处理器（GPU）处理超大规模图像计算瓶颈的数据并行化方案，例如整个载玻片扫描的样本。我们相信，这个软件，配备了最先进的技术创新，将是商业上有吸引力的骨骼肌研究社区，包括基础科学家，临床科学家和制药行业。具体目标是：1）开发、实施和验证骨骼肌组织的自动生物图像分析软件包; 2）开发新型在线更新的智能人工智能单元，使软件能够从错误中学习; 3）构建新型高性能计算单元，以实现快速和高通量的自动图像分析，其能够处理整个切片扫描的肌肉标本。所提出的分析方法将提供更一致、准确和客观的骨骼肌形态学特性量化，数据分析时间将减少100倍（独立版本）和2000倍（平行版本）。Cytoinformatics，LLC II期阶段的长期目标是应用软件分析人体肌肉活检样本的组织学/病理学，并将软件扩展到其他生物组织，如脂肪组织。