Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 10^9 or more pixels per channel, containing millions of cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing ROIs in an intuitive and cohesive manner. Building on a scope2screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.
翻译:使用光显微镜对组织进行检查是诊断许多疾病,特别是癌症的首要方法。高多重组织成像以这个基础为基础,收集多达60个分子信息渠道,加上细胞和组织形态,使用抗体沾染法收集。这为疾病生物学提供了独特的洞察,并有望帮助设计病人专用治疗方法。然而,在将由此产生的多变量图像数据进行视觉化方面,以及有效地支持数字环境中的癌症病理流程方面,仍然存在巨大差距。因此,我们开发了Slove2Screen,这是一个可缩缩放的软件系统,用于聚焦+文本探索和批注全滑动、高翻转、组织图象。我们用来分析每频道10+9或更多像素的100GB图像的尺度方法,其中含有数百万细胞的细胞。一个多学科专家、微观医师和病理学家小组确定了关键图像探索和注释任务,涉及查找、放大、量化和以直观和凝固的方式组织ROI。在范围2屏幕上建立隐喻比喻,我们把交互式透视和直径的图像和图像分析系统,在单一细胞结构结构结构结构结构中进行类似的分析,这些分析工具和图像和结构分析工具,这些分析工具和结构分析工具,在单一细胞结构结构结构和结构中进行。