# Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

arXiv: https://arxiv.org/abs/1810.07884 · HTML: https://mlsanity.com/paper/1810.07884/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 30 of 36)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Understanding Test-Time Augmentation](https://mlsanity.com/paper/2402.06892/) (2024)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [Uncertainty-Guided Progressive GANs for Medical Image Translation](https://mlsanity.com/paper/2106.15542/) (2021)
- [Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2301.09964/) (2023)
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- [Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction](https://mlsanity.com/paper/2308.09647/) (2023)
- [Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images](https://mlsanity.com/paper/2308.15141/) (2023)
- [Test-time generative augmentation for medical image segmentation](https://mlsanity.com/paper/2406.17608/) (2024)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging](https://mlsanity.com/paper/2406.17640/) (2024)
- [Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection](https://mlsanity.com/paper/2210.06512/) (2022)
- [Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty](https://mlsanity.com/paper/2301.00349/) (2023)
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- [Test-Time Augmentation Meets Variational Bayes](https://mlsanity.com/paper/2409.12587/) (2024)
- [Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout](https://mlsanity.com/paper/2501.11258/) (2025)
- [Adaptive Test-Time Augmentation for Low-Power CPU](https://mlsanity.com/paper/2105.06183/) (2021)
- [Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications](https://mlsanity.com/paper/2302.01440/) (2023)
- [TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision](https://mlsanity.com/paper/2407.14024/) (2024)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Uncertainty Quantification for Image-based Traffic Prediction across Cities](https://mlsanity.com/paper/2308.06129/) (2023)
- [Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systems](https://mlsanity.com/paper/2312.07602/) (2023)
- [How To Make Your Cell Tracker Say "I dunno!"](https://mlsanity.com/paper/2503.09244/) (2025)
- [SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis](https://mlsanity.com/paper/2201.08418/) (2022)
- [Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand](https://mlsanity.com/paper/2207.07622/) (2022)
- [Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models](https://mlsanity.com/paper/2211.06250/) (2022)
- [Variational Voxel Pseudo Image Tracking](https://mlsanity.com/paper/2302.05914/) (2023)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)

## Abstract (arXiv)

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.