# Deep Learning Based Brain Tumor Segmentation: A Survey

arXiv: https://arxiv.org/abs/2007.09479 · HTML: https://mlsanity.com/paper/2007.09479/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning](https://mlsanity.com/paper/2604.27277/) (2026)
- [Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection](https://mlsanity.com/paper/2210.06512/) (2022)
- [HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery](https://mlsanity.com/paper/2303.07717/) (2023)
- [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2505.13584/) (2025)
- [Bridging the Perception-Cognition Gap:Re-engineering SAM2 with Hilbert-Mamba for Robust VLM-based Medical Diagnosis](https://mlsanity.com/paper/2512.24013/) (2025)
- [Suggestive Annotation of Brain MR Images with Gradient-guided Sampling](https://mlsanity.com/paper/2206.01014/) (2022)
- [Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions](https://mlsanity.com/paper/2402.09249/) (2024)
- [InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction](https://mlsanity.com/paper/2605.02230/) (2026)
- [Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching](https://mlsanity.com/paper/2305.05789/) (2023)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)
- [Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology](https://mlsanity.com/paper/2309.07113/) (2023)
- [Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches](https://mlsanity.com/paper/2404.05341/) (2024)
- [Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers](https://mlsanity.com/paper/2406.06017/) (2024)
- [Brain Tumor Identification using Improved YOLOv8](https://mlsanity.com/paper/2502.03746/) (2025)
- [BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation](https://mlsanity.com/paper/2505.06133/) (2025)
- [Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?](https://mlsanity.com/paper/2510.07126/) (2025)
- [Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images](https://mlsanity.com/paper/2512.06531/) (2025)
- [Single-Slice-to-3D Reconstruction in Medical Imaging and Natural Objects: A Comparative Benchmark with SAM 3D](https://mlsanity.com/paper/2602.09407/) (2026)
- [Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment](https://mlsanity.com/paper/2602.21703/) (2026)
- [Bridging visual saliency and large language models for explainable deep learning in medical imaging](https://mlsanity.com/paper/2605.06197/) (2026)
- [D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2605.22249/) (2026)
- [A Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images](https://mlsanity.com/paper/2605.30510/) (2026)
- [CoMNet: A MedNeXt-CorrDiff Framework for Multi-Site Brain Tumor Segmentation](https://mlsanity.com/paper/2606.15305/) (2026)
- [RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation](https://mlsanity.com/paper/2607.05035/) (2026)
- [Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI](https://mlsanity.com/paper/2609.29785/) (2026)

## Abstract (arXiv)

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.