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Brain image segmentation is of great importance not only for clinical use but also for neuroscience research. Recent developments in deep neural networks (DNNs) have led to the application of DNNs to brain image segmentation, which required extensive human annotations of whole brain images. Annotating three-dimensional brain images requires laborious efforts by expert anatomists because of the differences among images in terms of their dimensionality, noise, contrast, or ambiguous boundaries that even prevent these experts from necessarily attaining consistency. This paper proposes a semi-supervised learning framework to train a DNN based on a relatively small number of annotated (labeled) images, named atlases, but also a relatively large number of unlabeled images by leveraging image registration to attach pseudo-labels to images that were originally unlabeled. We applied our proposed method to two different datasets: open human brain images and our original marmoset brain images. When provided with the same number of atlases for training, we found our method achieved superior and more stable segmentation results than those by existing registration-based and DNN-based methods.
This article was published in the following journal.
Name: Neural networks : the official journal of the International Neural Network Society
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A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
Tissue NECROSIS in any area of the brain, including the CEREBRAL HEMISPHERES, the CEREBELLUM, and the BRAIN STEM. Brain infarction is the result of a cascade of events initiated by inadequate blood flow through the brain that is followed by HYPOXIA and HYPOGLYCEMIA in brain tissue. Damage may be temporary, permanent, selective or pan-necrosis.
Adjustment of BRAIN WAVES from two or more neuronal groups within or across a brain structure (e.g., cortical and limbic brain structures) to become uniform in EEG oscillation patterns in response to a stimulus. It is interpreted as a brain integration sign during many processes such as learning, memory, and perception and involves reciprocal neural connections.