4.7 Article

Scope2Screen: Focus plus Context Techniques for Pathology Tumor Assessment in Multivariate Image Data

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3114786

关键词

Lenses; Tools; Task analysis; Data visualization; Annotations; Cancer; Rendering (computer graphics); Histopathology; Focus plus Context; Image Analysis

资金

  1. Ludwig Center at Harvard Medical School
  2. NIH/NCI [U2C-CA233262, NCI U2C-CA233280, NCI U54-CA225088]

向作者/读者索取更多资源

Inspection of tissues using a light microscope is crucial for diagnosing diseases like cancer, but there are challenges in visualizing and analyzing the vast amount of image data. Scope2Screen is a scalable software system developed to explore and annotate high-plex tissue images, aiding in the discovery of cancer-relevant image features.
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 individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen 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 with these regions. 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.

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