# Cascaded V-Net using ROI masks for brain tumor segmentation

arXiv: https://arxiv.org/abs/1812.11588 · HTML: https://mlsanity.com/paper/1812.11588/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 26 of 26)

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- [Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning](https://mlsanity.com/paper/2211.16213/) (2022)
- [Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision](https://mlsanity.com/paper/2307.05804/) (2023)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Towards Population Scale Testis Volume Segmentation in DIXON MRI](https://mlsanity.com/paper/2410.22866/) (2024)
- [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)
- [Localise to segment: crop to improve organ at risk segmentation accuracy](https://mlsanity.com/paper/2304.04606/) (2023)
- [K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment](https://mlsanity.com/paper/2307.04296/) (2023)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)

## Abstract (arXiv)

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture citeVNet, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.