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

arXiv: https://arxiv.org/abs/1705.03820 · HTML: https://mlsanity.com/paper/1705.03820/

## Key facts

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

## Built on by (most cited first, 30 of 42)

- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [TBraTS: Trusted Brain Tumor Segmentation](https://mlsanity.com/paper/2206.09309/) (2022)
- [Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation](https://mlsanity.com/paper/2208.06038/) (2022)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation](https://mlsanity.com/paper/2102.02033/) (2021)
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- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Connections between Numerical Algorithms for PDEs and Neural Networks](https://mlsanity.com/paper/2107.14742/) (2021)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors](https://mlsanity.com/paper/2105.01998/) (2021)
- [CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation](https://mlsanity.com/paper/2109.05443/) (2021)
- [RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images](https://mlsanity.com/paper/2207.07494/) (2022)
- [Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks](https://mlsanity.com/paper/2210.04285/) (2022)
- [A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring](https://mlsanity.com/paper/2102.03532/) (2021)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.07715/) (2021)
- [A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2202.06344/) (2022)
- [Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI](https://mlsanity.com/paper/2108.04267/) (2021)
- [A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials](https://mlsanity.com/paper/2201.07342/) (2022)
- [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](https://mlsanity.com/paper/2203.10507/) (2022)
- [A Note on the Regularity of Images Generated by Convolutional Neural Networks](https://mlsanity.com/paper/2204.10588/) (2022)
- [Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)](https://mlsanity.com/paper/2311.14148/) (2023)
- [Tree semantic segmentation from aerial image time series](https://mlsanity.com/paper/2407.13102/) (2024)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation](https://mlsanity.com/paper/2108.06772/) (2021)
- [Data Augmentation using Feature Generation for Volumetric Medical Images](https://mlsanity.com/paper/2209.14097/) (2022)
- [Unsupervised Brain Tumor Segmentation with Image-based Prompts](https://mlsanity.com/paper/2304.01472/) (2023)

## 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.