Histotools: scaling digital pathology curation tools for quality control, annotation, labeling, and dataset identification

摘要：随着最近整个玻片扫描仪被批准用于初步诊断，其中常规玻璃 组织病理学幻灯片被数字化，并在计算机显示器上提交给临床病理学家进行诊断， 大量新的未开发数据正在常规临床实践中被创造出来，并被放置在越来越多的数据湖中。在……里面 数字格式，这些完整的幻灯片图像(WSI)可以进行数字病理组学，即 提取与组织学形态、属性和关系相关的定量图像特征 WSIS中的对象。这些功能随后可用于在许多域中进行发现，例如 组织基因组学，认为表型表现与生物途径和基因有关 本体论。此外，低成本的基于非组织破坏性图像的伴随诊断分析(CDX) 可用于预测患者的预后和治疗反应。不幸的是，未加工的大件 仅有数据湖(如TCGA)不足以用于病理组学，而且通常需要大量的人类 管理工作：(I)对水资源综合利用进行细致的质量控制(即避免“垃圾入、出”)和 随后(Ii)精确地注释(例如，细胞边界)和标记(例如，细胞类型)组织对象。至 解决这些在管理数据湖方面的主要限制因素，我们建议发展我们的小规模组织工具 使用计算集群的原型，从而实现大型数字幻灯片规模的功能 存储库(DSR)：(I)通过识别人工制品(模糊性)对WSI进行健壮、可重复的质量控制的组织质量控制 和异常值(染色较差的幻灯片)，用于避免下游分析；(Ii)CohortFinder，用于识别 和批量影响的补偿，(Iii)快速注释器，用于通过一个 主动学习和机器学习的结合，(Iv)PatchSorter，用于改进组织学对象的子分类 机器学习。我们将评估OrganoTools的质量控制改进和两者的效率 通过以下方式对感兴趣的组织对象进行分段和标记：(A)现场管理和发布所使用的14K WSI 在我们的内部验证期间和(B)通过24个临床附属机构支持至少100k WSI的外部管理 来自除南极洲以外的每一大洲，在本提案期间，这些国家总共可获得2000多万份水资源倡议。 我们的验证用例旨在加快CDX领域的现有现场项目，包括4个 收集器官(乳房、肺、心脏、肾脏)、3种疾病(癌症、肾脏疾病和器官排斥)和WSIS 来自&gt；70个网站。这些队列特征将有助于确保我们用于管理数据湖的工具的通用性 创作，采用开源和可用性研究方法从合作者和 更大的研究社区。通过财团(ITCR、海王星)和网站(Github、TCIA)传播 将提高知名度和采用率。我们发布的工具和精心策划的数据集预计将引导 研究人员发起的CDX发现项目，以及创建他们自己的现场修剪数据湖。 总而言之，这一提议将产生基于数字病理学的精确医学研究。

ABSTRACT: With recent approval of whole slide scanners for primary diagnosis, wherein routine glass histopathology slides are digitized and presented to clinical pathologists for diagnosis on computer monitors, a wealth of new untapped data is being created in routine clinical practice and placed in growing data lakes. In digital format, these whole slide images (WSIs) can be subjected to digital pathomics, i.e., the process of extracting quantitative image features associated with morphology, attributes, and relationships of histologic objects in WSIs. These features can subsequently be employed for discovery in many domains such as histogenomics, which sees associating phenotypical presentations with biological pathways and gene ontologies. Additionally, low-cost non-tissue destructive image-based companion diagnostic assays (CDx) can be developed for predicting prognosis and treatment response of patients. Unfortunately, unprocessed large data lakes (e.g., TCGA) are not alone sufficient for pathomics, and often require an intractable amount of human curation effort in (i) performing meticulous quality control of WSI (i.e., avoid “garbage-in, garbage-out”) and subsequently (ii) precisely annotating (e.g., cell boundary) and labeling (e.g., cell type) histologic objects. To address these major limiting factors in curating data lakes, we propose developing our small-scale HistoTools prototypes to employ computing clusters and thus enable their function at the scale of large digital slide repositories (DSR): (i) HistoQC for robust, reproducible quality control of WSI by identifying artifacts (blurriness) and outliers (poorly stained slides) for avoidance in downstream analyses, (ii) CohortFinder for identification and compensation of batch affects, (iii) Quick Annotator for rapid computer aided annotation generation via a combination of active and machine learning, (iv) PatchSorter for improving sub-typing of histologic objects with machine learning. We will evaluate HistoTools for improvement of quality control and the efficiency of both segmenting and labeling histologic objects of interest via (a) onsite curation and release of the 14k WSI used during our internal validation and (b) supported external curation of at least 100k WSI via 24-clinical affiliates from every continent, except Antarctica, whom together have access to over 20 million WSI during this proposal. Our validation use cases are designed to expedite existing onsite projects in the CDx space, consisting of 4 organs (breast, lung, heart, kidney), 3 diseases (cancer, kidney disease, and organ rejection) and WSIs collected from >70 sites. These cohort characteristics will help ensure the generalizability of our tools for curated data lake creation, with open-source and usability study approaches employed to obtain feedback from collaborators and the larger research community. Dissemination through consortia (ITCR, NEPTUNE) and websites (Github, TCIA) will improve visibility and adoption. The tools and well-curated data sets we release are anticipated to bootstrap researcher-initiated CDx discovery projects, along with the creation of their own onsite manicured data lakes. Together, this proposal will engender digital pathology based precision medicine research.