A review of deep learning-based segmentation and registration of infant brain MRI

Magnetic resonance imaging (MRI) of the infant brain plays an important role in studying neonatal brain development as well as diagnosing and treating early brain diseases. Deep learning (DL)-based adult brain MR images processing techniques have developed relatively rapidly, with segmentation and registration techniques being the most common. The special characteristics of infant brain structure and rapid changes during developmental period make segmentation and registration of infant brain MR images challenging. This review critically assesses recent advances in infant brain MR images segmentation and registration. The performance of different DL models for processing infant brain MR images and metrics for evaluating the performance of segmentation and registration algorithms are discussed. This review selects and discusses more than 100 papers in related fields, covering technical aspects of data processing, neural network architectures, and attention mechanisms. We also provide an outlook on the future of infant brain MR images processing.