ReviewJournal of imaging2021
Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art.
Review in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.Brain sciences · 2026Review
- Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging.Scientific reports · 2026Article
- A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Review
- Casting wider than U-Nets: evaluating the role of novel machine learning applications in radiation therapy.BJR artificial intelligence · 2026Review
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- A novel sub-differentiable hausdorff loss combined with BCE for MRI brain tumor segmentation using UNet variants.Scientific reports · 2025Article
- Exploring deep learning and hybrid approaches in molecular subgrouping and prognostic-related genetic signatures of medulloblastoma.Chinese neurosurgical journal · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks.Life (Basel, Switzerland) · 2025Article
- MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks.Frontiers in computational neuroscience · 2025Article
- Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study.Frontiers in bioengineering and biotechnology · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.Journal of imaging informatics in medicine · 2024Article
- A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.Life (Basel, Switzerland) · 2024Article
- Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring.Tomography (Ann Arbor, Mich.) · 2024Article
- Applications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.Journal of statistical theory and practice · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.