1. Development of a semi-automatic segmentation technique based on mean magnetic resonance imaging intensity thresholding for volumetric quantification of plexiform neurofibromas
- Author
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Dorsa Sadat Kiaei, Ramy El-Jalbout, Jean-Claude Décarie, Sébastien Perreault, and Mathieu Dehaes
- Subjects
Plexiform neurofibroma (PN) ,Magnetic resonance imaging (MRI) ,Image segmentation ,Volumetric tumor quantification ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Rationale and objectives: Plexiform neurofibromas (PNs) are peripheral nerve tumors that occur in 25–50 % of patients with neurofibromatosis type 1. PNs may have complex, diffused, and irregular shapes. The objective of this work was to develop a volumetric quantification method for PNs as clinical assessment is currently based on unidimensional measurement. Materials and methods: A semi-automatic segmentation technique based on mean magnetic resonance imaging (MRI) intensity thresholding (SSTMean) was developed and compared to a similar and previously published technique based on minimum image intensity thresholding (SSTMini). The performance (volume and computation time) of the two techniques was compared to manual tracings of 15 tumors of different locations, shapes, and sizes. Performance was also assessed using different MRI sequences. Reproducibility was assessed by inter-observer analysis. Results: When compared to manual tracing, quantification performed with SSTMean was not significantly different (mean difference: 1.2 %), while volumes computed by SSTMini were significantly different (p
- Published
- 2024
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