Segmenting Magnetic Resonance images plays a critical role in radiotherapy, surgical planning and image-guided interventions. Traditional differential filter-based segmentation algorithms are predefined independently of image features and require extensive post processing. Convolutional Neural Networks (CNNs) are regarded as a powerful visual model that yields hierarchies of features learned from image data, however, its usage is limited in medical imaging field as it requires large-scale data for training. In this paper, we propose a simple binary detection algorithm to bridge CNNs and medical imaging for accurate medical image segmentation. It applies high-capacity CNNs to extract features from image data. When labeled training medical images are scarce, the proposed algorithm splits data into small regions, and labels them to boost training data size automatically. Rather than replaces classic segmentation methods, this paper presents an alternative that is unique and provides more desirable segmentation results....
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