Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners

Gliomas are the most common and aggressive form of all brain tumours, leading to a very short survival time at their highest grade. Hence, swift and accurate treatment planning is key. Magnetic resonance imaging (MRI) is a widely used imaging technique for the assessment of these tumours but the large amount of data generated by them prevents rapid manual segmentation, the task of dividing visual input into tumorous and non-tumorous regions. Hence, reliable automatic segmentation methods are required. This paper proposes, tests and validates two different approaches to achieving this. Firstly, it is hypothesised that co-registering multiple MRI modalities into a single volume will result in a more time and memory efficient approach which captures the same, if not more, information resulting in accurate segmentation. Secondly, it is hypothesised that training models independently on different MRI modalities allow models to specialise on certain labels or regions, which can then be ensembled to achieve improved predictions. These hypotheses were tested by training and evaluating 3D U-Net models on the BraTS 2020 data set. The experiments show that these hypotheses are indeed valid.