Keywords: Brain Tumor Segmentation, Topology, Image Segmentation, Deep Learning, Convolutional Networks
TL;DR: Contribution: Exploiting a topology loss to enforce hierarchical ordering in brain tumor segmentions
Abstract: Fully Convolutional Networks (FCNs) are widely used in medical image analysis for segmentation tasks. However, most FCNs fail to directly incorporate image geometry such as topology and boundary smoothness during segmentation. The sub-regions of the brain tumor in MRI images follow a particular topological order. However, the automatic segmentation of these brain tumors with conventional FCNs may violate the topological structure of brain tumors. FCNs could be constrained with a topological loss to enforce structured predictions. This paper presents the effect of such topological loss on brain tumor segmentation using the BraTS dataset.
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