Abstract:
Automated segmentation of brain tumour from multimodal MR images is pivotal for
the analysis and monitoring of disease progression. As gliomas are malignant and
heterogeneous, efficient and accurate segmentation techniques are used for the
successful delineation of tumours into intra-tumoural classes. Deep learning
algorithms outperform on tasks of semantic segmentation as opposed to the more
conventional, context-based computer vision approaches. Extensively used for
biomedical image segmentation, Convolutional Neural Networks have significantly
improved the state-of-the-art accuracy on the task of brain tumour segmentation. In
this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a
U-Net, in a significant yet straightforward combinative technique that results in better
and accurate predictions. Both models were trained separately on the BraTS-19
challenge dataset and evaluated to yield segmentation maps which considerably
differed from each other in terms of segmented tumour sub-regions and were
ensembled variably to achieve the final prediction. The suggested ensemble achieved
dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and
tumour core, respectively, on the validation set, performing favourably in comparison
to the state-of-the-art architectures currently available.
