Collection and fuzzy estimation of truth labels in glial tumour segmentation studies

Abstract

In this work, we propose a novel behavioural comparison strategy specifically oriented to accuracy assessment in MRI glial tumour segmentation studies. A salient aspect of the proposed strategy is the use of the fuzzy set framework in modelling visual inspection and interpretation processes. In particular, a reference estimation strategy based on fuzzy connectedness principles is designed to merge individual labels and produce a common segmentation. The estimation is based exclusively on highly reliable partial information provided by experts. Interaction is then drastically limited compared with a complete manual tracing, leaving the estimation of the complete segmentation to the fuzzy connectedness method. A set of experiments was conceived and conducted to evaluate the contribution of the solutions proposed in the process of truth label collection and reference data estimation. A comparison analysis was also developed to see whether our method could constitute a worthy alternative to well-known and state-of-the-art solutions.