SYNCS: Synthetic Data and Contrastive Self-Supervised Training for
  Central Sulcus Segmentation
Vladyslav Zalevskyi Kristoffer Hougaard Madsen
Bipolar disorder (BD) and schizophrenia (SZ) are severe mental disorders with
profound societal impact. Identifying risk markers early is crucial for
understanding disease progression and enabling preventive measures. The Danish
High Risk and Resilience Study (VIA) focuses on understanding early disease
processes, particularly in children with familial high risk (FHR).
Understanding structural brain changes associated with these diseases during
early stages is essential for effective interventions. The central sulcus (CS)
is a prominent brain landmark related to brain regions involved in motor and
sensory processing. Analyzing CS morphology can provide valuable insights into
neurodevelopmental abnormalities in the FHR group. However, segmenting the
central sulcus (CS) presents challenges due to its variability, especially in
adolescents. This study introduces two novel approaches to improve CS
segmentation: synthetic data generation to model CS variability and
self-supervised pre-training with multi-task learning to adapt models to new
cohorts. These methods aim to enhance segmentation performance across diverse
populations, eliminating the need for extensive preprocessing.