Contextual information extraction in brain tumour segmentation

Abstract

Abstract Automatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.

Keywords

• attention gate
• attentional residual dropout block
• context aware 3D ARDUNet
• convolutional neural networks
• modified 3D U‐net
• residual dropout block