Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation

TL;DR

This paper introduces a multi-stage deep neural network architecture utilizing deep layer aggregation for improved brain tumor segmentation from MRI images, evaluated on the BraTS 2020 dataset with promising results.

Contribution

The proposed cascade of three deep layer aggregation networks is a novel approach that enhances feature response by incorporating previous stage outputs and MRI channels.

Findings

Achieved Dice scores of 0.8858, 0.8297, 0.7900 for tumor regions.

Demonstrated effective segmentation on BraTS 2020 dataset.

Reported Hausdorff distances indicating precise boundary delineation.

Abstract

Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge dataset, where we evaluated our proposal in the BraTS 2020 Validation and Test sets. In the Test set, the experimental results achieved a Dice score of 0.8858, 0.8297 and 0.7900, with an Hausdorff Distance of 5.32 mm, 22.32 mm and 20.44 mm for the whole tumor, core tumor and enhanced tumor, respectively.