Automatic segmentation of healthy tissues and tumours in patient brain images using 3D fully convolutional neural networks

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It is estimated that over 55,000 Canadians are currently living with a brain tumour. Patients with gliomas, the most frequent primary brain tumour in adults, still have very poor prognosis despite considerable advances in research. High-grade glioma patients have a median life expectancy of two years or less, and low-grade gliomas come with a life expectancy of several years. In either case, neuroimaging protocols are employed before and after treatment in order to estimate disease progression, surgical planning and effect. Current clinical protocol involves analysis of the patient images by a radiologist, where rudimentary qualitative and quantitative metrics are employed, such as the manual measurements of tumour size, a process that is time-consuming, subjective and potentially inconsistent. The goals of this project are to develop robust, accurate and fully automatic tissue segmentation techniques that can identify both healthy and diseased tissues when applied to real, multimodal, clinical MRI, with the long-term potential benefit of improving patient diagnosis, surgical planning and follow-up. This includes the development of new machine learning (e.g. deep learning) techniques to accurately detect and segment (1) tumours into their constituent sub-structures (e.g. tumour core, edema) and (2) healthy tissues (e.g. white matter) in multi-channel patient MRI. Although deep learning frameworks have been incredibly successful at a wide variety of tasks in computer vision, their adaptation to medical image detection and segmentation, particularly of pathological structures, is still in its infancy. This is due to a multitude of new challenges presented in the context of noisy, multi-modal 3D images, and to a shortage of large-scale datasets required for training. Medical image analysis research would benefit from the development of new mathematical models and analytical tools that could potentially improve patient care and outcome, including the savings in time and the improvement in accuracy of pre-surgical planning and post-operative follow-up.