# Multi-step Cascaded Networks for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/1908.05887 · HTML: https://mlsanity.com/paper/1908.05887/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation](https://mlsanity.com/paper/2304.10864/) (2023)
- [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://mlsanity.com/paper/2201.12785/) (2022)
- [TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2204.12185/) (2022)
- [Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2206.06575/) (2022)
- [A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2202.06344/) (2022)
- [Category Guided Attention Network for Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/2203.15383/) (2022)
- [Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2310.18656/) (2023)
- [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)
- [Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation](https://mlsanity.com/paper/2307.12180/) (2023)
- [PGR-Net: Prior-Guided ROI Reasoning Network for Brain Tumor MRI Segmentation](https://mlsanity.com/paper/2603.21626/) (2026)

## Abstract (arXiv)

Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment. In this paper, we propose a multi-step cascaded network which takes the hierarchical topology of the brain tumor substructures into consideration and segments the substructures from coarse to fine .During segmentation, the result of the former step is utilized as the prior information for the next step to guide the finer segmentation process. The whole network is trained in an end-to-end fashion. Besides, to alleviate the gradient vanishing issue and reduce overfitting, we added several auxiliary outputs as a kind of deep supervision for each step and introduced several data augmentation strategies, respectively, which proved to be quite efficient for brain tumor segmentation. Lastly, focal loss is utilized to solve the problem of remarkably imbalance of the tumor regions and background. Our model is tested on the BraTS 2019 validation dataset, the preliminary results of mean dice coefficients are 0.886, 0.813, 0.771 for the whole tumor, tumor core and enhancing tumor respectively. Code is available at https://github.com/JohnleeHIT/Brats2019