A Comprehensive Review on Predicting Specific Substructures in the Brainstem using Deep Learning Techniques

The article offers an in-depth examination of multiple deep learning analytics have been applied to brainstem images to identify features segmentation of substructures such as the pons, medulla, and midbrain. It examines the incorporation of multi-modal brain imaging data, such as PET, DTI, and MRI scans, to improve these models' accuracy. Important issues include inter-subject variability, data scarcity, and the requirement for high-quality annotated datasets, are discussed. Furthermore, the review highlights recent developments in model fusion strategies and the integration of multiple data sources to improve predictive capabilities. The practical applications of these models in clinical settings, including pre-surgical planning, disease diagnosis, and understanding neurological conditions, are explored. The review evaluates various studies to establish conclusions regarding the topic under research 20 studies published between 2023 and 2024, identifying significant contributions and outlining potential areas for additional study and advancement in the area of deep learning-driven brainstem substructure prediction.