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.