Deep learning tools for the automated analysis of hematopathology whole slide images and the development of prognostic algorithms for hematopoietic stem cell transplant recipients

项目概要/摘要 骨髓是造血的主要部位，其检查是诊断的中心 和血液病患者的管理。临床血液病理学的病理切片 服务代表了人类骨髓生物学真实世界数据的宝库。但人类 检查是时间密集型的，其定量精度有限，阻止了我们进行大规模检查的能力， 规模研究或确定新的疾病形态标志物。机器学习和图像处理 方法可以应用于分析全载玻片图像（WSIs），这将导致我们的改进 对血液系统的理解，以及我们诊断和管理血液系统的能力， 疾病 这项研究的目标是建立基于深度学习的工具，用于自动分类 骨髓穿刺，并使用这些工具来研究造血干细胞移植中的造血作用 受惠人士核心假设是可以开发自动化方法来进行分类， 表征和定量人骨髓穿刺物上的细胞形态，并且这些工具 可以识别预测造血干细胞移植后结果的形态学特征。长期 目标是开发一套用于定量分析血液病理学WSIs的人工智能工具 这可以用来提高我们对造血的理解， 通过开发数字血液病理学工具、染色剂和生物标志物， 研究和临床应用。这种方法是创新的，因为它应用了尖端的图像分析 临床病理学人类骨髓标本的定量和可扩展研究的技术 档案，以推动新的生物学见解和临床生物标志物的发现。

PROJECT SUMMARY/ABSTRACT The bone marrow is the primary site of hematopoiesis and its examination is central to the diagnosis and management of patients with hematological diseases. Pathology slides from clinical hematopathology services represent a treasure trove of real-world data on the biology of human bone marrow. However, human examination is time-intensive and limited in its quantitative precision, preventing our ability to perform large- scale studies or identify new morphologic markers of disease. Machine learning and image processing methods can be applied to the analysis of whole-slide images (WSIs), which will lead to improvements in our understanding of the hematological system, as well as our ability to diagnose and manage hematological diseases. The objective of this research is to build deep learning-based tools for the automated classification of bone marrow aspirates and use these tools to study hematopoiesis in hematopoietic stem cell transplant recipients. The central hypothesis is that automated methods can be developed for the classification, characterization, and quantification of cell morphology on human bone marrow aspirates and that these tools can identify morphologic features predictive of outcome after hematopoietic stem cell transplant. The long-term goal is to develop a suite of artificial intelligence tools for the quantitative analysis of hematopathology WSIs that can be used to improve our understanding of hematopoiesis and our management of patients with hematologic diseases through the development of digital hematopathology tools, stains, and biomarkers for research and clinical applications. This approach is innovative because it applies cutting-edge image analysis technology to the quantitative and scalable study of human bone marrow specimens from clinical pathology archives to drive discovery of new biological insights and clinical biomarkers.