Abstract
The present work deals with segmentation of Glial Tumors in MRI images focusing on critical aspects in manual labeling and reference estimation for segmentation validation purposes. A reproducibility analysis was conducted confirming the presence of different sources of uncertainty involved in the process of manual segmentation and responsible of high intra-operator and inter-operator variability. Technical and conceptual solutions aimed to reduce operator variability and support in the reference estimation process are integrated in GliMAn (Glial Tumor Manual Annotator), an application allowing to view and manipulate MRI volumes and implementing a label fusion strategy based on fuzzy connectedness. A set of experiments was conceived and conducted to evaluate the contribution of the solutions proposed in the process of manual segmentation and reference data estimation.
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Binaghi, E., Pedoia, V., Lattanzi, D., Monti, E., Balbi, S., Minotto, R. (2015). On the Evaluation of Automated MRI Brain Segmentations: Technical and Conceptual Tools. In: Tavares, J., Natal Jorge, R. (eds) Developments in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-13407-9_1
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