ArticleAJNR. American journal of neuroradiology2024
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.
Article in AJNR. American journal of neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Extended Reality in Neurosurgery : Surgical Planning, Navigation, Education, and Patient Communication.Journal of Korean Neurosurgical Society · 2026Article
- [Research progress of multimodal magnetic resonance imaging brain tumor segmentation based on fused neural network model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early,Brain communications · 2026Article
- Deep learning-based automatic segmentation and CT elastography radiomics for preoperative prediction of right recurrent laryngeal nerve lymph node metastasis in esophageal cancer.Frontiers in oncology · 2026Article
- Application of multi-scale dynamic enhancement based on deep neural network and CT urinary tract secretory phase image fusion in the diagnosis of urinary system diseases.BMC medical imaging · 2025Article
- Imaging Clusters of Pediatric Low-Grade Glioma Are Associated with Distinct Molecular Characteristics.AJNR. American journal of neuroradiology · 2025Article
- Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.Neuro-oncology · 2025Review
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- Comment on Dalboni da Rocha et al. Artificial Intelligence for Neuroimaging in Pediatric Cancer.Cancers · 2025Article
- The Evolving Landscape of Radiomics in Gliomas: Insights into Diagnosis, Prognosis, and Research Trends.Cancers · 2025Review
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Exploring neonatal brain tumors: a narrative review about epidemiology, classification, and management.Annals of medicine and surgery (2012) · 2025Review
- Review
- Accurate and robust segmentation of cerebral distal small arteries by DVNet with dual contextual path and vascular attention enhancement.Quantitative imaging in medicine and surgery · 2025Article
- Comparison of brain normalization software and lesion compensation techniques in chronic perinatal stroke imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging.Frontiers in neuroscience · 2025Review
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical dataset.Neuro-oncology advancesArticle
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15 authors.
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Abstract
background and purposeTumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND
methodsMultiparametric preoperative MR imaging scans of 339 pediatric patients (
resultsThe mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (
conclusionsThe pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.
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