ArticleInternational journal of computer assisted radiology and surgery2020
DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning.Radiological physics and technology · 2026Article
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Quantum-inspired optimization of transformer-capsule networks for accurate brain tumor segmentation and classification.Scientific reports · 2026Article
- A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.Brain informatics · 2026Article
- A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points.Scientific reports · 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
- Advancing Precision Medicine in Degenerative Cervical Myelopathy.Journal of clinical medicine · 2025Review
- A bias-resilient client selection analysis for federated brain tumor segmentation.Scientific reports · 2025Article
- Clinical evaluation of two glioblastoma delineation methods based on neural networks.Technical innovations & patient support in radiation oncology · 2025Article
- Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.Scientific reports · 2025Article
- Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging.Communications medicine · 2025Article
- Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation.Frontiers in neuroinformatics · 2025Article
- GAME-Net: an ensemble deep learning framework integrating Generative Autoencoders and attention mechanisms for automated brain tumor segmentation in MRI.Frontiers in computational neuroscience · 2025Article
- VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.PloS one · 2025Article
- A foundation model-driven multi-view collaborative framework for semi-supervised 3D medical image segmentation.Frontiers in medicine · 2025Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
- Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.IBRO neuroscience reports · 2024Article
- NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.Journal of medical systems · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
purposeGliomas 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.
methodsThe 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.
resultsThe 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.
conclusionThis 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/.
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