1. Collection and fuzzy estimation of truth labels in glial tumour segmentation studies
- Author
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Elisabetta Binaghi, Sergio Balbi, and Valentina Pedoia
- Subjects
Fuzzy set ,Biomedical Engineering ,Computational Mechanics ,Scale-space segmentation ,02 engineering and technology ,Tracing ,computer.software_genre ,Machine learning ,Fuzzy logic ,030218 nuclear medicine & medical imaging ,Tumour segmentation ,03 medical and health sciences ,0302 clinical medicine ,manual labelling ,0202 electrical engineering, electronic engineering, information engineering ,MRI segmentation ,validation ,fuzzy connectedness ,reference estimation ,common agreement ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Mathematics ,business.industry ,Computer Science Applications ,Visual inspection ,Salient ,020201 artificial intelligence & image processing ,Data mining ,Artificial intelligence ,business ,computer - 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 sta...
- Published
- 2014
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