Robust statistical fusion of image labels

Bennett A. Landman, Andrew J. Asman, Andrew G. Scoggins, John A. Bogovic, Fangxu Xing, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

Abstract

Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability

Original languageEnglish (US)
Article number6046134
Pages (from-to)512-522
Number of pages11
JournalIEEE transactions on medical imaging
Volume31
Issue number2
DOIs
StatePublished - Feb 1 2012

Keywords

  • Data fusion
  • delineation
  • labeling
  • parcellation
  • simultaneous truth and performance level estimation (STAPLE)
  • statistical analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Robust statistical fusion of image labels'. Together they form a unique fingerprint.

Cite this