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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588102
• 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.

拟议的研究利用了模式识别、图像处理和图像处理方面的进展。 处理，计算几何和机器学习开发新的 解决医学成像领域的问题。我特别 我正在进一步研究计算机辅助医学诊断细胞学 乳腺癌细针穿刺活检和薄血涂片的成像， 疟疾检测对于这些类型的细胞学图像， 目标是开发强大的，可访问的，快速的，准确的医疗 诊断工具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， 或者作为专家系统来帮助非专业的执业医师。 计算机辅助医学诊断或恶性肿瘤分级的过程可以 可以分为两个主要阶段：从医学图像中提取特征， 根据具体医疗问题对图像进行分类。一 所提出的工作的目标是确定改进的特征集 用于特定类型的细胞学图像。许多经常使用的 用于医学图像分类问题的特征需要精确的 在确定这些特征之前进行图像分割。所以我 研究提高分割精度和速度的方法， 通过扩展我以前的工作， 使用Hough变换到使用活动轮廓模型， 有效地使用纹理、每像素分类器和形状建模。我们 并将该研究扩展到新医疗器械的准确分割， 成像技术，如非常大的全载玻片虚拟图像，3D图像， 以及使用扩展聚焦成像（Extended Focal Imaging，EFL）创建的图像。 所提出的研究的另一个目标是提高 乳腺癌细针细胞学图像的恶性分类 活组织检查具体而言，我们研究恶性肿瘤诊断的问题， 确定载玻片是恶性还是良性，以及恶性分级 我们使用布卢姆-理查森恶性肿瘤分级为了实现这一点， 通过优化的特征集，我们将开发分类器系统， 提高灵敏度，特别是最低的错误率 底片。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整。第三个目标 这项研究的目的是提高对疟疾寄生虫的检测， 血液涂片载玻片的细胞学图像。基于我们之前完成的 血细胞计数框架，我们将开发一个计算机辅助疟疾检测系统， 确定疟疾存在并返回准确感染情况的系统 厚血涂片中每RBC计数的计数;并区分 在薄的血涂片中发现了四种疟原虫。我们将开发 具有改进的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疗领域的更大趋势 图像，以开发计算机辅助医疗诊断工具， 并促进临床医生和病理学家的工作。所有的发展都是 设计成使用通常可用的显微镜和计算资源。为 例如，在疟疾肆虐的国家的农村地区， 经常没有专家做血液载玻片的显微镜筛查， 疟疾通过对这些幻灯片的自动分析， 在我的研究中提出的辅助专家系统将有助于填补知识 具有必要专业知识的专家与从业者之间的差距 实际上是检查细胞学切片，从而更好地照顾病人。