Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification

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elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “The software has been used in several research projects, including [2], [8], [15], [24], [50], [55], [67]–[73], and is still under active development.”
The Rotterdam Study: 2010 objectives and design update (2007, methodology): “Fully automated methods are applied to quantify atrophy of brain tissues and structures and the severity of white matter lesions [343–345] Automated hippocampal segmentation has been successfully applied on multi-scanner acquired MR images (on scans acquired in the Rotterdam Scan Study in 1995 [346]”
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (2016, background): “A popular approach for brain image segmentation is the use of (population speciﬁc) atlases [10]– [17], but also pattern recognition methods are used [18], [19], sometimes in combination with an atlas-based approach [20]–[22].”
White matter lesion extension to automatic brain tissue segmentation on MRI (2009, unknown): “A threshold of 0.7 is chosen, which is shown by Vrooman et al. (2007) to be a good threshold value for these tissue types, especially CSF.”