DeepAI
Log In Sign Up

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

12/30/2020
by   Haozhe Jia, et al.
0

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.

READ FULL TEXT

page 3

page 9

02/10/2022

HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

In our previous work, i.e., HNF-Net, high-resolution feature representat...
11/02/2020

nnU-Net for Brain Tumor Segmentation

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. T...
05/24/2021

Brain tumour segmentation using a triplanar ensemble of U-Nets

Gliomas appear with wide variation in their characteristics both in term...
09/09/2017

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

Deep learning has quickly become the weapon of choice for brain lesion s...
09/20/2018

Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images

Recently deep learning has been playing a major role in the field of com...
09/16/2020

Brain tumour segmentation using cascaded 3D densely-connected U-net

Accurate brain tumour segmentation is a crucial step towards improving d...