# Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/1709.02967 · HTML: https://mlsanity.com/paper/1709.02967/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2202.06344/) (2022)
- [A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors](https://mlsanity.com/paper/2109.14775/) (2021)
- [M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast](https://mlsanity.com/paper/2507.20582/) (2025)
- [Tumor-Centered Patching for Enhanced Medical Image Segmentation](https://mlsanity.com/paper/2308.12168/) (2023)

## Abstract (arXiv)

Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive regions of enhancing and non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net architecture in a tree structure to create segmentations for edema, non-enhancing tumor, and enhancing tumor regions. Specifically, training was configured such that the whole tumor region including edema was predicted first, and its output segmentation was fed as input into separate models to predict enhancing and non-enhancing tumor. Our method was trained and evaluated on the publicly available BraTS dataset, achieving Dice scores of 0.882, 0.732, and 0.730 for whole tumor, enhancing tumor and tumor core respectively.