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Published on: December 15, 2023
Development of a semi-automatic segmentation technique based on mean magnetic resonance imaging intensity
Dorsa Sadat Kiaei1,2, Ramy El-Jalbout2,3, Jean-Claude Décarie3
1Institute of Biomedical Engineering, University of Montréal, Montréal, Canada.
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 < .0001, mean difference: 13.4 %). Volumes quantified by SSTMean were also significantly different than the ones assessed by SSTMini (p < .0001). Using SSTMean, volumes quantified with short TI inversion recovery, T1-, and T2-weighted imaging were not significantly different. Computation times used by SSTMean and SSTMini were significantly lower than for manual segmentation (p < .0001). The highest difference measured by two users was 8 cm3.
Conclusion:
Our method showed accuracy compared to a current gold standard (manual tracing) and reproducibility between users. The refined segmentation threshold and the possibility to define multiple regions-of-interest to initiate segmentation may have contributed to its performance. The versatility and speed of our method may prove useful to better monitor volumetric changes in lesions of patients enrolled in clinical trials to assessing response to therapy.
