# On-the-Fly Data Augmentation for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2509.24973 · HTML: https://mlsanity.com/paper/2509.24973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [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)
- [Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks](https://mlsanity.com/paper/2403.09262/) (2024)
- [Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models](https://mlsanity.com/paper/2409.08143/) (2024)

## Abstract (arXiv)

Robust segmentation across both pre-treatment and post-treatment glioma scans can be helpful for consistent tumor monitoring and treatment planning. BraTS 2025 Task 1 addresses this by challenging models to generalize across varying tumor appearances throughout the treatment timeline. However, training such generalized models requires access to diverse, high-quality annotated data, which is often limited. While data augmentation can alleviate this, storing large volumes of augmented 3D data is computationally expensive. To address these challenges, we propose an on-the-fly augmentation strategy that dynamically inserts synthetic tumors using pretrained generative adversarial networks (GliGANs) during training. We evaluate three nnU-Net-based models and their ensembles: (1) a baseline without external augmentation, (2) a regular on-the-fly augmented model, and (3) a model with customized on-the-fly augmentation. Built upon the nnU-Net framework, our pipeline leverages pretrained GliGAN weights and tumor insertion methods from prior challenge-winning solutions. An ensemble of the three models achieves lesion-wise Dice scores of 0.79 (ET), 0.749 (NETC), 0.872 (RC), 0.825 (SNFH), 0.79 (TC), and 0.88 (WT) on the online BraTS 2025 validation platform. This work ranked first in the BraTS Lighthouse Challenge 2025 Task 1- Adult Glioma Segmentation.