Several approaches based on convolutional neural networks (CNNs) are only able to process 2D images while most brain data consists of 3D volumes. Recent network architectures which have demonstrated promising results are able to process 3D images. In this work, we propose an adapted approach based on a CNN to process 3D contextual information in brain MRI scans for the challenging task of brain tumour segmentation. Our CNN is trained end-to-end on multi-modal MRI volumes and is able to predict segmentation for the binary case, which segments the whole tumour, and multi-class case, which segments the whole tumour (WT), tumour core (TC) and enhancing tumour (ET). Our network includes multiple layers of dilated convolutions and autofocus convolutions with residual connections to improve segmentation performance. Autofocus layers consist of multiple parallel convolutions each with a different dilation rate. We replaced standard convolutional layers with autofocus layers to adaptively change the size of the effective receptive field to generate more powerful features. Experiments with our autofocus settings on the BraTS 2018 glioma dataset show that the proposed method achieved average Dice scores of 83.92 for WT in the binary case and 66.88, 55.16, 64.13 for WT, TC and ET, respectively, in the multi-class case. We introduce the first publicly and freely available NiftyNet-based implementation of the autofocus convolutional layer for semantic image segmentation.
|Title of host publication
|In Annual Conference on Medical Image Understanding and Analysis
|Subtitle of host publication
|Part of the Communications in Computer and Information Science book series (CCIS)
|Number of pages
|Published - 8 Jul 2020