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.

翻译：双相情感障碍（BD）和精神分裂症（SZ）是严重影响社会的严重精神疾病。早期识别风险标志物对于理解疾病进展和采取预防措施至关重要。丹麦高风险与韧性研究（VIA）重点关注早期疾病过程，特别是针对有家族高风险（FHR）的儿童。了解这些疾病在早期阶段相关的脑结构变化对于有效干预至关重要。中央沟（CS）是与运动和感觉处理相关脑区的重要脑标志。分析CS形态可为FHR组神经发育异常提供宝贵见解。然而，由于中央沟（CS）的变异性（尤其在青少年中），其分割面临挑战。本研究提出两种新方法以改进CS分割：生成合成数据以模拟CS变异性，以及采用多任务学习的自监督预训练使模型适应新队列。这些方法旨在提升跨人群分割性能，同时避免大量预处理需求。