End-to-end learning of brain tissue segmentation from imperfect labeling

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many potentially faulty intermediate steps. In order to streamline the segmentation, we introduce a deep learning model that is based on volumetric dilated convolutions, subsequently reducing both processing time and errors. Compared to its competitors, the model has a reduced set of parameters and thus is easier to train and much faster to execute. The contrast in performance between the dilated network and its competitors becomes obvious when both are tested on a large dataset of unprocessed human brain volumes. The dilated network consistently outperforms not only another state-of-the-art deep learning approach, the up convolutional network, but also the ground truth on which it was trained. Not only can the incredible speed of our model make large scale analyses much easier but we also believe it has great potential in a clinical setting where, with little to no substantial delay, a patient and provider can go over test results.Comment: Published as a conference paper at IJCNN 2017 Preprint versio

Computer generative method on brain tumor segmentation in MRI images

Computer generative method has been used for a long time in brain tumor segmentation tasks on magnetic resonance images. The popularity of machine learning also prompts people to explore the use of generative methods to better train their segmentation models. At the early stage, brain tumor segmentation competitions like BraTS 2012 used computer synthetic MR images with tumor to solve the lack of enough data in the training set, and now, with the rise of computer generative models in deep learning, more researchers have started to work on this track to find a better solution for the task. This thesis addresses the implementation and analysis of some existing methods, specifically a tumor synthetic tool called TumorSim and a competition winning deep learning model that incorporates variational auto-encoder as a generative model. This thesis also reports on an experiment that uses imperfect segmented tumors from simple models as the input to a generative adversarial network to generate a better result.Ope