Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

This study demonstrates the effectiveness of propidium iodide as a reliable marker for detecting dead or dying cells in frozen liver tissue sections. By comparing propidium iodide staining with the widely used Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, both methods showed consistent results in disease models such as alcohol-induced fibrosis and Western diet-induced fatty liver. Additionally, propidium iodide was successfully co-stained with other fluorescent markers, like phalloidin (for actin filaments) and antibodies targeting collagen, enabling detailed spatial analysis of dying cells within tissue. This multiplex approach allows for a deeper understanding of tissue organization and cell death localization, particularly in complex conditions like liver fibrosis. Moreover, our results suggest that propidium iodide staining can be applied beyond current models, offering a more accessible and cost-effective alternative to traditional methods, like TUNEL. Furthermore, its integration with other markers enables simultaneous analysis of immune responses and tissue damage, making it a powerful tool for future studies on liver disease and other inflammatory conditions. This technique has the potential to advance research into disease mechanisms and improve the evaluation of novel therapeutic strategies targeting tissue regeneration and inflammation control.