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Glioblastoma dataset CFB-GBM v2.0 released with enhanced segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have released CFB-GBM v2.0, an expanded dataset for glioblastoma research. This new version significantly increases the completion rate of Gross Tumour Volume (GTV) delineations to 97% using a fine-tuned nnU-Net model, validated by oncologists. The dataset now includes volumetric RANO 2.0 response labels, pre-computed brain masks, and radiomic features, along with updated WHO classification documentation. CFB-GBM v2.0 is available on The Cancer Imaging Archive (TCIA). AI

IMPACT Enhances AI-driven research capabilities for glioblastoma treatment response prediction and personalized medicine.

RANK_REASON The item is an academic paper detailing a new dataset release for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Glioblastoma dataset CFB-GBM v2.0 released with enhanced segmentation

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Alexandre G. Leclercq, No\'emie N. Moreau, Hugo Audebert, Andros Nassar, Thomas Cochin, Thomas Leleu, Lo\"ic Le Henaff, Alexis Desmonts, Yoann Poirier, Aur\'elie Dubru, Laura Guillemette, Pascal Lecoeur, K\'evin Lemasson, Cyril Jaudet, S\'ebastien Bougle… ·

    CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

    arXiv:2608.17884v1 Announce Type: new Abstract: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support…

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