Automated Brain Tumour Segmentation Using Deep Fully Convolutional Residual Networks

08/12/2019
by   Indrajit Mazumdar, et al.
27

Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net. To deal with class imbalance effectively, we have formulated a weighted Dice loss function. We found that increasing the depth of the 'U' shape beyond a certain level results in a decrease in performance, so it is essential to choose an optimum depth. We also found that 3D contextual information cannot be captured by a single 2D network that is trained with patches extracted from multiple views whereas an ensemble of three 2D networks trained in multiple views can effectively capture the information and deliver much better performance. Our method obtained Dice scores of 0.79 for enhancing tumour, 0.90 for whole tumour, and 0.82 for tumour core on the BraTS 2018 validation set and its performance is comparable to the state-of-the-art methods.

READ FULL TEXT

page 2

page 5

research
12/12/2020

HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation

The brain tumor segmentation task aims to classify tissue into the whole...
research
02/28/2018

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Quantitative analysis of brain tumors is critical for clinical decision ...
research
07/03/2017

Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

The Dice score is widely used for binary segmentation due to its robustn...
research
12/25/2017

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

As a basic task in computer vision, semantic segmentation can provide fu...
research
10/26/2020

Does contextual information improve 3D U-Net based brain tumor segmentation?

Effective, robust and automatic tools for brain tumor segmentation are n...
research
09/16/2020

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

Accurate brain tumour segmentation is a crucial step towards improving d...
research
02/12/2019

Extended 2D Volumetric Consensus Hippocampus Segmentation

Hippocampus segmentation plays a key role in diagnosing various brain di...

Please sign up or login with your details

Forgot password? Click here to reset