# Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs

arXiv: https://arxiv.org/abs/2001.02040 · HTML: https://mlsanity.com/paper/2001.02040/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation](https://mlsanity.com/paper/2406.01425/) (2024)
- [Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining](https://mlsanity.com/paper/2504.03474/) (2025)
- [Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks](https://mlsanity.com/paper/2108.01941/) (2021)
- [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)
- [Abdominal multi-organ segmentation in CT using Swinunter](https://mlsanity.com/paper/2309.16210/) (2023)
- [Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment](https://mlsanity.com/paper/2602.21703/) (2026)

## Abstract (arXiv)

Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the fundamental vision tasks necessary for diagnosis and treatment planning of the disease. Previous years winning methods were all deep-learning based, thanks to the advent of modern GPUs, which allow fast optimization of deep convolutional neural network architectures. In this work, we explore best practices of 3D semantic segmentation, including conventional encoder-decoder architecture, as well combined loss functions, in attempt to further improve the segmentation accuracy. We evaluate the method on BraTS 2019 challenge.