# Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand

arXiv: https://arxiv.org/abs/2207.07622 · HTML: https://mlsanity.com/paper/2207.07622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 15 Jul 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [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)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation](https://mlsanity.com/paper/1810.07884/) (2018)
- [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018)
- [Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs](https://mlsanity.com/paper/2001.02040/) (2020)
- [Multi-step Cascaded Networks for Brain Tumor Segmentation](https://mlsanity.com/paper/1908.05887/) (2019)
- [A Novel Domain Adaptation Framework for Medical Image Segmentation](https://mlsanity.com/paper/1810.05732/) (2018)
- [Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans](https://mlsanity.com/paper/2010.10612/) (2020)

## Abstract (arXiv)

Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed. The post-processing techniques were the main responsible for the results obtained in the segmentations. The segmented regions were the whole tumor, the tumor core, and the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934, 0.8376, and 0.8113, respectively. These results reached the state of the art in glioma segmentation determined by the winners of the challenge.