Welcome to QuIP – a web accessible tool set designed to support analysis, management, and exploration of whole slide tissue images for cancer research. This is an NIH funded multi-site collaborative effort between Stony Brook University, Emory University, Oak Ridge National Labs, and Yale University. Click on any of the colored buttons to launch the associated tool.

Image and Results Viewer

You can view a collection of ~3200 TCGA images from Brain(LGG & GBM), Breast(BRCA), Lung(LUAD), and Pancreatic(PAAD) cancer whole slide images and segmentation results. You can view the images, nuclear segmentations, as well as aggreement amongst segmentation algorithms that are presented as overlaid heatmaps. Click on the magnifier icon to choose algorithm results and heatmaps. You may zoom in, zoom out, and pan the images. Mouse Click: Zoom in, Shift-Click: Zoom out.

Visual Feature Analytics

This suite of interactive tools work together to allow interrogation of multiple parameters including: cancer type, age, patient demographics, nuclear morphologic features and survival, gene expression, and the interaction that impact survival curve, allowing direct visual evaluation as well as computer extracted information to evaluate patient survival.

An interactive tool to allow patient-level feature exploration across multiple dimensions.

A visual analytics platform for exploring slide-level imaging features generated by analysis of wholeslide tissue images

Clinical Data Query

An interactive dashboard to explore interrelations between patient demographics, pathologist-generated diagnostic keywords, and outcomes based on publicly-available TCGA data. Through integration into FeatureExplorer and FeatureScape individual corresponding images and results can be explored further.

GitHub repositories

Links to our GitHub repositories for FeatureDB, analysis codes, Slicer pathology extensions, and Region Templates framework

FeatureDB
Analysis
Slicer pathology extensions
Region Templates framework

Docker Hub image repository