Rabanaque, D.; Regalado, M.; Benítez, R.; Rabanaque, S.; Agut, T.; Carreras, N.; Mata, C. Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images. J. Imaging2023, 9, 145.
Rabanaque, D.; Regalado, M.; Benítez, R.; Rabanaque, S.; Agut, T.; Carreras, N.; Mata, C. Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images. J. Imaging 2023, 9, 145.
Rabanaque, D.; Regalado, M.; Benítez, R.; Rabanaque, S.; Agut, T.; Carreras, N.; Mata, C. Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images. J. Imaging2023, 9, 145.
Rabanaque, D.; Regalado, M.; Benítez, R.; Rabanaque, S.; Agut, T.; Carreras, N.; Mata, C. Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images. J. Imaging 2023, 9, 145.
Abstract
The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows considerable increase of the brain surface. In premature childrens, these alterations take place outside the womb that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic GUI platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians, for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches, pre-processing tools, and is friendly designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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