Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking. (4th July 2017)

• Record Type: Journal Article
• Authors: Wan, Y.; Hansen, C.
• Is Part Of: Computer graphics forum. Volume 36:Number 3(2017)
• Page Start: 479
• Page End: 489
• Publication Date: 2017-07-04
• Subjects: Categories and Subject Descriptors (according to ACM CCS) -- I.4.6 [Segmentation]: Pixel classification— -- I.4.8 [Scene Analysis]: Tracking— -- J.3 [Life and Medical Sciences]: Biology and Genetics—; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13204
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three‐dimensionally and densely packed in a time‐dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real‐world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data‐independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow – cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.