Related Experiment Video
Updated: Jul 21, 2025

State of the Art Cranial Ultrasound Imaging in Neonates
Published on: February 2, 2015
Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images
David Rabanaque1, Maria Regalado1, Raul Benítez1,2,3
1Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain.
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 for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment 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 Graphical User Interface (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 and pre-processing tools and is user-friendly and 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.
More Related Videos
06:36Author Spotlight: High-Resolution Imaging of Mouse Neonate Brains – A Micro-CT Protocol with Lugol's Solution Contrast Agent
Published on: May 19, 2023
16:01An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
Published on: September 24, 2017