Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

Background: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.

TARGET-seq: Linking single-cell transcriptomics of human dopaminergic neurons with their target specificity.

Dopaminergic (DA) neurons exhibit significant diversity characterized by differences in morphology, anatomical location, axonal projection pattern, and selective vulnerability to disease. More recently, scRNAseq has been used to map DA neuron diversity at the level of gene expression. These studies have revealed a higher than expected molecular diversity in both mouse and human DA neurons.

Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs.

Background And Purpose: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility.