Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI

Abstract : Classification forests, as discussed in Chapter 2, have a series of advantageous properties which make them a very good choice for applications in medical image analysis. Classification forests are inherent multi-label classifiers (which allows for the simultaneous segmentation of different tissues), have good generalization properties (which is important as training data is often scarce in medical applications), and are able to deal with very high-dimensional feature spaces (which allows the use of non-local and context-aware features to describe the input data). In this chapter we demonstrate how classification forests can be used as a basic building block to develop state of the art systems for medical image analysis in two challenging applications. These applications perform the segmentation of two different types of brain lesions based on 3D multi-channel magnetic resonance images (MRI) as input. More specifically, we discuss (1) the segmentation of the individual tissues of high-grade brain tumor lesions, and (2) the segmentation of multiple-sclerosis lesions.
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Ezequiel Geremia, Darko Zikic, Olivier Clatz, Bjoern Menze, Ben Glocker, et al.. Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI. Criminisi, Antonio and Shotton, J. Decision Forests for Computer Vision and Medical Image Analysis, Springer London, pp.245-260, 2013, Advances in Computer Vision and Pattern Recognition, 978-1-4471-4928-6. ⟨10.1007/978-1-4471-4929-3_17⟩. ⟨hal-00931809⟩

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