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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=677102
• 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.

拟议的研究利用模式识别，图像处理，计算几何和机器学习的进步，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疗诊断，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，准确的医疗 * 诊断工具，以提供自动独立的第二意见，以帮助 * 避免错误/疏忽，病理学家，临床医生和医学专家，* 或作为一个专家系统，以帮助非专业医生。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针对特定医学问题对图像进行分类。所提出的工作的一个目标是为特定类型的细胞学图像确定改进的特征集。许多经常使用的用于医学图像分类问题的 * 功能，需要准确的 * 图像分割之前，确定这些功能。因此，我正在研究方法，以提高细胞学图像中细胞分割的准确性和速度，通过扩展我以前的工作，从使用Hough变换到使用活动轮廓模型，结合有效使用纹理，每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 * 成像技术的准确分割，例如非常大的全切片虚拟图像，3D图像 * 和使用扩展聚焦成像（EFL）创建的图像。这项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，我们研究的问题，恶性肿瘤的诊断，以 * 确定是否载玻片是恶性或良性的，和恶性肿瘤分级 *，我们使用布卢姆-理查森恶性肿瘤分级。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，* 提高灵敏度，特别是最小的假阴性率。由于分类器必须在临床环境中工作，因此我们将 * 调整分类器以进行自动参数调整。这项研究的第三个目标是提高血涂片细胞学图像中疟原虫的检测能力。在我们以前的完整血细胞计数框架的基础上，我们将开发一个计算机辅助疟疾检测系统，以确定疟疾的存在，并在厚血涂片中返回每个红细胞计数的准确感染计数;并在薄血涂片中区分疟疾寄生虫的四个物种。我们将开发具有改进的敏感率的分类器系统。我在健康信息学方面的工作有助于医学图像的更大趋势，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。所有的发展 * 旨在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，经常没有专家对血片进行疟疾的显微镜筛查。通过对这些切片的自动分析，在我的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切片的从业者之间的知识差距，从而导致更好的患者护理。