Convolutional neural network based brain MRI segmentation

Baydar, Bora
Visualization of the inner parts of human body is crucial in modern medicine and magnetic resonance imaging(MRI) is one of the widely used medical imaging methods. Manual analysis of MRIs, however, wastes the valuable time of experts. Development of an automatic segmentation method for brain MRIs can save time spent by the experts and can avoid human error factor. In this thesis, convolutional neural network (CNN) based methods are applied on brain MRI segmentation problem. The basic architectures used are FCN-8 and U-NET. Performance of different approaches has been analyzed by focusing on structural modifications, upsampling methods, activation functions, loss functions, pre-processing and post-processing methods. For the activation functions, ReLU, LReLU, PReLU and tanh are experimented. Histogram matching, normalization and histogram equalization have been applied for pre-processing. Conditional random fields and a 3 dimensional connected component analysis are separately integrated to the network as post-processors. Results are compared in terms of dice score, sensitivity and specificity. The experimental results show that the combination of two separate U-Nets has the best performance. Dilation modules also improve the results when inserted on a shal-low network. When combined with additional residual connections, they have alsoimproved the overall results. Inception modules do not provide a remarkable performance improvement. ReLU and PReLU have shown the best performance. No significant difference have been observed between results obtained from different upsampling methods, although bilinear interpolation, despite being non-trainable, has slightly better results.


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Functional magnetic resonance imaging (fMRI) utilizes the change in the oxygenation of blood to predict active areas in the brain. fMRI consists of multiple low resolution whole brain images, for which, the contrast difference in corresponding voxels among all images are studied. In this study, an fMRI simulator has been developed which generates customized 4D fMRI data that can be used as a ground truth for comparing/benchmarking different fMRI analysis methods. This simulator can be also used for educatio...
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Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approa...
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Pamir, Zahide; Acartürk, Cengiz; Boyacı, Hüseyin; Department of Cognitive Sciences (2014)
The present study has employed psychophysics and functional magnetic resonance imaging (fMRI) methodologies. The aim of the study is to investigate the role of bottom-up and top-down processing of luminance in contrast perception. In particular, since it is thought that visual illusions occur as a result of top-down processing by means of visual context, the present study investigates how luminance in context affects contrast perception by using brightness illusion. In other words, the purpose of the study ...
CEREBRA: a 3-D visualization and processing tool for brain network extracted from fMRI data
Nasır, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we introduce a new tool, CEREBRA, for visualizing 3D network of human brain, extracted from the functional magnetic resonance imaging (fMRI) data. The tool aims to visualize the selected voxels as the nodes of the network and the edge weights are estimated by modeling the relationships among the voxel time series as a set of linear regression equations. This way, researchers can analyze the active brain regions/voxels and observe the interactions among them by analyzing the edge weights and ...
Coil sensitivity map calculation using biot-savart law at 3 tesla and parallel imaging in MRI
Esin, Yunus Emre; Alpaslan, Ferda Nur; Department of Computer Engineering (2017)
Coil spatial sensitivity map is considered as one of the most valuable data used in parallel magnetic resonance imaging (MRI) reconstruction. In this study, a novel sensitivity map extraction method is introduced for phased-array coils. Proposed technique uses Biot-Savart law with coil shape information and low-resolution phase image data to form sensitivity maps. The performance of this method has been tested in the parallel image reconstruction task using sensitivity encoding technique. In MRI, coil sensi...
Citation Formats
B. Baydar, “Convolutional neural network based brain MRI segmentation,” M.S. - Master of Science, Middle East Technical University, 2018.