  This paper presents an annotated dataset of brain MRI images designed to
advance the field of brain symmetry study. Magnetic resonance imaging (MRI) has
gained interest in analyzing brain symmetry in neonatal infants, and challenges
remain due to the vast size differences between fetal and adult brains.
Classification methods for brain structural MRI use scales and visual cues to
assess hemisphere symmetry, which can help diagnose neonatal patients by
comparing hemispheres and anatomical regions of interest in the brain. Using
the Developing Human Connectome Project dataset, this work presents a dataset
comprising cerebral images extracted as slices across selected portions of
interest for clinical evaluation . All the extracted images are annotated with
the brain's midline. All the extracted images are annotated with the brain's
midline. From the assumption that a decrease in symmetry is directly related to
possible clinical pathologies, the dataset can contribute to a more precise
diagnosis because it can be used to train deep learning model application in
neonatal cerebral MRI anomaly detection from postnatal infant scans thanks to
computer vision. Such models learn to identify and classify anomalies by
identifying potential asymmetrical patterns in medical MRI images. Furthermore,
this dataset can contribute to the research and development of methods using
the relative symmetry of the two brain hemispheres for crucial diagnosis and
treatment planning.
