REVIEW 6 cited by
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
read the original abstract
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
Forward citations
Cited by 6 Pith papers
-
Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis
A patient-specific energy manifold learned from baseline multiparametric MRI provides a geometric reference system for tracking longitudinal tissue changes through energy and displacement analysis in sequence space.
-
Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis
Patient-specific energy manifolds from baseline mpMRI scans act as fixed geometric references to monitor longitudinal evolution of voxel distributions in sequence space for neuro-oncology proof-of-concept cases.
-
BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation
BiSegMamba is a bidirectional tri-oriented Mamba architecture that improves performance and reduces FLOPs in 3D medical image segmentation across brain, cardiac, abdominal, and vascular tasks.
-
Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis
A baseline-trained energy manifold in MRI intensity space showed progressive drift toward the tumour regime in a recurrence case but not in a stable case, suggesting a segmentation-free longitudinal monitoring signal.
-
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
A radiomic-guided subtyping and lesion-wise ensemble pipeline delivers segmentation performance comparable to top entries on diverse BraTS 2025 brain tumor datasets.
-
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
A radiomic-guided, lesion-aware ensemble with per-cluster post-processing yields strong segmentation across pediatric, meningioma, and metastasis MRI cohorts, though 'top-ranked' parity is asserted rather than shown.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.