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Deep learning for feature discovery in brain MRIs for patient-level classification with applications to multiple sclerosis

University of British Columbia

Network architectures and training strategies are crucial considerations in applying deep learning to neuroimaging data, but attaining optimal performance still remains challenging, because the images involved are high-dimensional and the pathological patterns to be modeled are often subtle. Additional challenges include limited annotations, heterogeneous modalities, and sparsity of certain image types. In this thesis, we have developed detailed methodologies to overcome these challenges for automatic feature extraction from multimodal neuroimaging data to perform image-level classification and segmentation, with applications to multiple sclerosis (MS). We developed our new methods in the context of four MS applications. The first was the development of an unsupervised deep network for MS lesion segmentation that was the first to use image features that were learned completely automatically, using unlabeled data. The deep-learned features were then refined with a supervised classifier, using a much smaller set of annotated images. We assessed the impact of unsupervised learning by observing the segmentation performance when the amount of unlabeled data was varied. Secondly, we developed an unsupervised learning method for modeling joint features from quantitative and anatomical MRIs to detect early MS pathology, which was novel in the use of deep learning to integrate high-dimensional myelin and structural images. Thirdly, we developed a supervised model that extracts brain lesion features that can predict conversion to MS in patients with early isolated symptoms. To efficiently train a convolutional neural network on sparse lesion masks and to reduce the risk of overfitting, we proposed utilizing the Euclidean distance transform for increasing information density, and a combination of downsampling, unsupervised pretraining and regularization during training. The fourth method models multimodal features between brain lesion and diffusion patterns to distinguish between MS and neuromyelitis optica, a neurological disorder similar to MS, to support differential diagnosis. We present a novel hierarchical multimodal fusion architecture that can improve joint learning of heterogeneous imaging modalities. Our results show that these models can discover subtle patterns of MS pathology and provide enhanced classification and prediction performance over the imaging biomarkers previously used in clinical studies, even with relatively small sample sizes.

Text

2018-05-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66085

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/