Extending Upon a Transfer Learning Approach for Brain Tumour Segmentation

Abstract

The incidence of gliomas has been on the rise and are the most common malignant brain tumours diagnosed upon medical appointments. A common approach to identify and diagnose brain tumours is to use Magnetic Resonance Imaging (MRI) to pinpoint tumour regions. However, manual segmentation of brain tumours is highly time-consuming and challenging due to the multimodal structure of MRI scans coupled with the task of delineating boundaries of different brain tissues. As such, there is a need for automated and accurate segmentation techniques in the medical domain to reduce both time and task complexity. Various Deep Learning techniques such as Convolutional Neural Networks (CNN) and Fully Connected Networks (FCN) have been introduced to address this challenge with promising segmentation results on various datasets. FCNs such as U-Net in recent literature achieve state-of-the-art performance on segmentation tasks and have been adapted to tackle various domains. In this paper, we propose an improved extension upon an existing transfer learning method on the Brain Tumour Segmentation (BraTS) 2020 dataset and achieved marginally better results compared to the original approach.