Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas

16 Jan 2025

Abstract excerpt

An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. Brats-Toolkit and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Glioma Diagnosis and Treatment
Share to Radiomics and Machine Learning in Medical Imaging

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39820086
DOI: 10.1038/s41598-025-85400-9

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 10 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areasPMID 39820086Read ↗

Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system.Semantic proximity Read ↗

IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.Semantic proximity Read ↗

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activitiesSemantic proximity Read ↗

The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) annotated multimodal MRI Dataset.Semantic proximity Read ↗

Non-invasive tumor probability maps developed using autopsy tissue identify novel areas of tumor beyond the imaging-defined marginSemantic proximity Read ↗

Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-NetSemantic proximity Read ↗

Contrast-Enhancing Tumor Margin Detection in Gliomas using Non-Contrast MRI: From Human-Only to Human-AI Assisted AssessmentSemantic proximity Read ↗

Quantitative Mapping of Individual Voxels in the Peritumoral Region of Glioblastoma to Distinguish Between Tumor Infiltration and EdemaSemantic proximity Read ↗

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networksSemantic proximity Read ↗

A Consensus Definition of Supratotal Resection for Anatomically Distinct Primary Glioblastoma: An AANS/CNS Section on Tumors Survey of Neurosurgical OncologistsSemantic proximity Read ↗

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional StudySemantic proximity Read ↗

Brain tumor segmentation using a combination of graph cut and optimized U-Net with genetic algorithm: An efficient hybrid framework for enhanced medical image analysisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.