# H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task

arXiv: https://arxiv.org/abs/2012.15318 · HTML: https://mlsanity.com/paper/2012.15318/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps](https://mlsanity.com/paper/2312.02608/) (2023)
- [Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?](https://mlsanity.com/paper/2101.00232/) (2021)
- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging](https://mlsanity.com/paper/2202.05268/) (2022)
- [DB-SAM: Delving into High Quality Universal Medical Image Segmentation](https://mlsanity.com/paper/2410.04172/) (2024)
- [Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2302.02521/) (2023)
- [Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction](https://mlsanity.com/paper/2401.00587/) (2023)
- [Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images](https://mlsanity.com/paper/2209.09930/) (2022)
- [Fairboard: a quantitative framework for equity assessment of healthcare models](https://mlsanity.com/paper/2604.09656/) (2026)
- [Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2605.16880/) (2026)

## Abstract (arXiv)

In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances (95%) of 26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.