2 March 2018 Feature extraction using convolutional neural networks for multi-atlas based image segmentation
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Abstract
Multi-atlas based image segmentation in conjunction with pattern recognition based label fusion strategies has achieved promising performance in a variety of image segmentation problems, including hippocampus segmentation from MR images. The pattern recognition based label fusion consists of image feature extraction and pattern recognition components. Since the feature extraction component plays an important role in the pattern recognition based label fusion, a variety of feature extraction methods have been proposed to extract image features, including texture features and random projection features. However, these feature extraction methods are not adaptive to different segmentation problems. Following the success of convolutional neural networks in image feature extraction, we propose a feature extraction method based on convolutional neural networks for multi-atlas based image segmentation. The proposed method has been validated based on 135 T1 magnetic resonance imaging (MRI) scans and their hippocampus labels provided by the EADC-ADNI harmonized segmentation protocol. We also compared our method with state-of-the-art pattern recognition based MAIS methods, including Local Label Learning and Random Local Binary Patterns. The experimental results have demonstrated that our method could achieve competitive hippocampus segmentation performance over the alternative methods under comparison.
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Xuesong Yang, Xuesong Yang, Yong Fan, Yong Fan, } "Feature extraction using convolutional neural networks for multi-atlas based image segmentation", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057439 (2 March 2018); doi: 10.1117/12.2293876; https://doi.org/10.1117/12.2293876
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