Abstract
Tissue segmentation of infants could lead to early diagnosis of neurological disorders, potentially enabling early interventions. However, the challenge of tissue quantification is increased due to the very dynamic changes that happen as brain development advances over the course of the first year. One of the structural processes is the myelination which causes limited contrast between gray and white matter tissue on T1-weighted and T2-weighted magnetic resonance images at around six to nine months. In recent years, as a result of the MICCAI brain MRI segmentation challenge in 6-month old infants (iSeg17 and iSeg19), there has been an increase in interest in this complex task. In this work, we propose two methodologies to overcome issues of erroneous segmentation on the border between gray and white matter, based on knowledge-guided U-Net for segmenting the isointense infant brain. First, segmentation was guided using a prior of white matter obtained from an atlas for developing infants. Second, segmentation was focused on the low-intensity contrast boundary between white and gray matter. Experimental results on the subjects of iSeg19 challenge display the potential of utilizing the white matter prior as input for segmentation. Overall, its utilization leads to results that are closer to the brain anatomy with smoother and connected white matter regions.
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22 September 2022
A correction has been published.
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Acknowledgement
The PARENT project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Innovative Training Network 2020. Grant Agreement N 956394.
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Vujadinovic, J., Viana, J.S., de la Rosa, E., Ortibus, E., Sima, D.M. (2022). Knowledge-Guided Segmentation of Isointense Infant Brain. In: Licandro, R., Melbourne, A., Abaci Turk, E., Macgowan, C., Hutter, J. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2022. Lecture Notes in Computer Science, vol 13575. Springer, Cham. https://doi.org/10.1007/978-3-031-17117-8_10
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