Computer Assisted Cytological Medical Image Analysis

计算机辅助细胞学医学图像分析

• 批准号: RGPIN-2014-04929
• 负责人: Fevens, Thomas
• 金额: $ 1.46万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566831
• 关键词: Computer; Assisted; Cytological; Medical; Image

The proposed research leverages advances in Pattern Recognition, Image Processing, Computational Geometry and Machine Learning to develop novel solutions to address problems in the area of Medical Imaging. Specifically, I am furthering my research on Computer Aided Medical Diagnosis for cytological imagery of breast cancer fine needle biopsies, and of thin blood smears for malaria detection. For these types of cytological images, my long term objective is to develop robust, accessible, rapid, and accurate Medical Diagnosis tools to provide an automatic independent second opinion, to help avoid errors/oversights, to Pathologists, Clinicians and Medical Specialists, or act as an expert system to aid the non-specialist medical practitioner. The process of computer-assisted medical diagnosis or malignancy grading can be described in two main stages: feature extraction from medical imagery and classification of the imagery with respect to specific medical issues. One objective of the proposed work is the determination of improved feature sets for specific types of cytological images. Many of the frequently used features used for medical image classification problems require accurate image segmentation prior to determining these features. Therefore, I am studying approaches to improve the accuracy and speed of the segmentation of cells in cytological images by extending my previous work ranging from the use of the Hough transform to using active contours models, to incorporate effective use of texture, per-pixel classifiers, and shape modeling. We are also extending this research to the accurate segmentation of new medical imaging technologies such as very large full slide virtual images, 3D images, and images created with extended focal imaging (EFI). Another objective of the proposed research is to improve the accuracy of the malignancy classification in cytological images of breast cancer fine needle biopsies. Specifically, we study the problems of malignancy diagnosis to determine whether the slide is malignant or benign, and of malignancy grading where we use the Bloom-Richardson malignancy grading. To accomplish this, with optimized feature sets, we will develop systems of classifiers that improve sensitivity rates, particularly with minimal rates of false negatives. Since the classifiers have to work in a clinical setting, we will adapt the classifiers to do automatic parameter tuning. The third objective of the proposed research is to improve the detection of malaria parasites in cytological images of blood smear slides. Building on our previous complete blood count framework, we will develop a computer-assisted malaria detection system to determine the presence of malaria and return accurate infection counts per RBC counts in thick blood smears; and to differentiate between the four species of the malarial parasite in thin blood smears. We will develop systems of classifiers with improved sensitivity rates. My work in Health Informatics contributes to the larger trend in medical imagery to develop computer assisted medical diagnosis tools to complement and facilitate the work of Clinicians and Pathologists. All developments are designed to use commonly available microscopy and computing resources. For example, in the rural areas of malaria stricken countries there are frequently no specialists to do microscopic screening of blood slides for malaria. Through the automatic analysis of these slides, the computer assisted expert systems proposed in my research would help fill the knowledge gap between a specialist with the necessary expertise and the practitioner actually examining cytological slides, thus leading to better patient care.

该研究利用模式识别、图像处理、计算几何和机器学习方面的进展来开发新的解决方案，以解决医学成像领域的问题。具体来说，我正在进一步研究计算机辅助医学诊断，用于乳腺癌细针活检的细胞学图像，以及用于疟疾检测的薄血涂片。对于这些类型的细胞学图像，我的长期目标是开发强大的、可访问的、快速的和准确的医学诊断工具，为病理学家、临床医生和医学专家提供自动独立的第二意见，以帮助避免错误/疏忽，或者作为一个专家系统来帮助非专业医疗从业者。计算机辅助医学诊断或恶性肿瘤分级的过程可以分为两个主要阶段：从医学图像中提取特征和根据特定医学问题对图像进行分类。提出的工作的一个目标是确定改进的特征集为特定类型的细胞学图像。许多用于医学图像分类问题的常用特征需要在确定这些特征之前进行准确的图像分割。因此，我正在研究通过扩展我以前的工作来提高细胞学图像中细胞分割的准确性和速度的方法，从使用霍夫变换到使用活动轮廓模型，结合有效使用纹理、逐像素分类器和形状建模。我们还将这项研究扩展到新的医学成像技术的精确分割，例如非常大的全幻灯片虚拟图像，3D图像和使用扩展焦成像（EFI）创建的图像。本研究的另一个目的是提高乳腺癌细针活检细胞学图像中恶性肿瘤分类的准确性。具体来说，我们研究了恶性肿瘤诊断的问题，以确定幻灯片是恶性的还是良性的，以及我们使用布鲁姆-理查森恶性肿瘤分级的恶性肿瘤分级。为了实现这一目标，通过优化的特征集，我们将开发分类器系统，以提高灵敏度，特别是以最小的假阴性率。由于分类器必须在临床环境中工作，我们将调整分类器以进行自动参数调优。本研究的第三个目标是提高血液涂片细胞学图像中疟疾寄生虫的检测。在我们以前的完整血球计数框架的基础上，我们将开发一种计算机辅助疟疾检测系统，以确定疟疾的存在，并根据厚血涂片中的红细胞计数返回准确的感染计数；并通过薄血涂片区分四种疟原虫。我们将开发具有更高灵敏度的分类器系统。我在健康信息学方面的工作有助于医学图像的大趋势，即开发计算机辅助医疗诊断工具，以补充和促进临床医生和病理学家的工作。所有的开发都是为了使用常用的显微镜和计算资源。例如，在疟疾肆虐的国家的农村地区，经常没有专家对血液玻片进行疟疾的显微筛查。通过对这些载玻片的自动分析，我在研究中提出的计算机辅助专家系统将有助于填补具有必要专业知识的专家与实际检查细胞学载玻片的从业者之间的知识差距，从而导致更好的患者护理。