Abstract
In this work we propose a feature-based segmentation approach that is domain independent. While most existing approaches are based on application-specific hand-crafted features, we propose a framework for learning features from data itself at multiple scales and depth. Our features can be easily integrated into classifiers or energy-based segmentation algorithms. We test the performance of our proposed method on two MICCAI grand challenges, obtaining the top score on VESSEL12 and competitive performance on BRATS2012.
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Kiros, R., Popuri, K., Cobzas, D., Jagersand, M. (2014). Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_4
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DOI: https://doi.org/10.1007/978-3-319-10581-9_4
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