# DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

arXiv: https://arxiv.org/abs/2004.12333 · HTML: https://mlsanity.com/paper/2004.12333/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

## Built on by (most cited first, 21 of 21)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution](https://mlsanity.com/paper/2212.09310/) (2022)
- [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024)
- [Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients](https://mlsanity.com/paper/2209.01402/) (2022)
- [Multimodal Masked Autoencoder Pre-training for 3D MRI-Based Brain Tumor Analysis with Missing Modalities](https://mlsanity.com/paper/2505.00568/) (2025)
- [Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI](https://mlsanity.com/paper/2112.06554/) (2021)
- [Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients](https://mlsanity.com/paper/2211.11025/) (2022)
- [Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans](https://mlsanity.com/paper/2303.05126/) (2023)
- [Brain Tumor Segmentation from MRI Images using Deep Learning Techniques](https://mlsanity.com/paper/2305.00257/) (2023)
- [Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis](https://mlsanity.com/paper/2410.18698/) (2024)
- [Deepfake Image Generation for Improved Brain Tumor Segmentation](https://mlsanity.com/paper/2307.14273/) (2023)
- [VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation](https://mlsanity.com/paper/2410.21304/) (2024)
- [Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining](https://mlsanity.com/paper/2504.03474/) (2025)
- [Scale-aware Adaptive Supervised Network with Limited Medical Annotations](https://mlsanity.com/paper/2601.01005/) (2026)
- [An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Images](https://mlsanity.com/paper/2206.04056/) (2022)
- [A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points](https://mlsanity.com/paper/2512.17566/) (2025)
- [SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusion](https://mlsanity.com/paper/2604.24109/) (2026)
- [SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation](https://mlsanity.com/paper/2605.22572/) (2026)

## Abstract (arXiv)

Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full resolution probability map. Based on modified U-Net architecture, different CNN models such as Residual Neural Network (ResNet), Dense Convolutional Network (DenseNet), and NASNet have been utilized in this study. Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of Brain Tumor Segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open-source and freely available at https://github.com/razeineldin/DeepSeg/.