mlsanityData to 14 Oct 2026

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

arXiv 1705.03820 ↗First posted 10 May 2017cs.CVcited by 42

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Abstract (arXiv)

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2017.

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Citing papers per year: 2021: 16; 2022: 13; 2023: 5; 2024: 4; 2025: 4.

42 papers.

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  1. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
  2. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 33
  3. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  4. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 21
  5. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  6. 2021Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentationcited by 17
  7. 2022Distributed Contrastive Learning for Medical Image Segmentationcited by 16
  8. 2021Detection and severity classification of COVID-19 in CT images using deep learningcited by 12
  9. 2021Connections between Numerical Algorithms for PDEs and Neural Networkscited by 10
  10. 20213D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Frameworkcited by 9
  11. 2022Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysiscited by 9
  12. 2021Effects of Image Size on Deep Learningcited by 8
30 more
  1. 2021Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priorscited by 8
  2. 2021CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregationcited by 7
  3. 2022RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound imagescited by 6
  4. 2022Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networkscited by 5
  5. 2021A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouringcited by 4
  6. 2021Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectivescited by 3
  7. 2021Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptationcited by 3
  8. 2022A Data Augmentation Method for Fully Automatic Brain Tumor Segmentationcited by 3
  9. 2021Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRIcited by 2
  10. 2022A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materialscited by 2
  11. 2022Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesionscited by 2
  12. 2022A Note on the Regularity of Images Generated by Convolutional Neural Networkscited by 2
  13. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2
  14. 2024Tree semantic segmentation from aerial image time seriescited by 2
  15. 2021A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Predictioncited by 1
  16. 2021Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentationcited by 1
  17. 2022Data Augmentation using Feature Generation for Volumetric Medical Imagescited by 1
  18. 2023Unsupervised Brain Tumor Segmentation with Image-based Promptscited by 1
  19. 2025Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decodingcited by 1
  20. 2021In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagerycited by 0
  21. 2022Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural networkcited by 0
  22. 2023Medical Image Analysis using Deep Relational Learningcited by 0
  23. 2023A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentationcited by 0
  24. 2023Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRIcited by 0
  25. 2024Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentationcited by 0
  26. 2024LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentationcited by 0
  27. 2024multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Informationcited by 0
  28. 2025Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomascited by 0
  29. 2025Assessment of Using Synthetic Data in Brain Tumor Segmentationcited by 0
  30. 2025Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNNcited by 0