Atlas of Classifiers for Brain MRI Segmentation
We present a conceptually novel framework for brain tissue segmentation based on an Atlas of Classifiers (AoC). The AoC allows a statistical summary of the annotated datasets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segmentation. Specifically, we consider an AoC as a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights (a few for each voxel) represent the training dataset, which might be huge. Segmentation of a new image is therefore immediate and only requires the calculation of the LR outputs based on the respective voxel-wise features. Moreover, the AoC construction is independent of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities.
The proposed method has been applied to publicly available datasets for the segmentation of brain MRI tissues and is shown to outreach commonly used methods. Promising results were obtained also for multi-modal, cross-modality MRI segmentation.
- 2.Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006)Google Scholar
- 3.Fischl, et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)Google Scholar
- 6.Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. MEDIA 24(1), 205–219 (2015)Google Scholar
- 7.Leemput, V., et al.: Automated model-based tissue classification of MR images of the brain. TMI 18(10), 897–908 (1999)Google Scholar
- 8.Mendrik, et al.: MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans. Comput. Intelll. Neurosci. 2015, 16 (2015)Google Scholar
- 9.Moeskops, P., et al.: Automatic segmentation of MR brain images with a convolutional neural network. TMI 35(5), 1252–1261 (2016)Google Scholar
- 10.Pohl, K.M., et al.: A hierarchical algorithm for MR brain image parcellation. TMI 26(9), 1201–1212 (2007)Google Scholar
- 11.Rohlfing, T.: Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. TMI 31(2), 153–163 (2012)Google Scholar
- 16.Wells, W.M., et al.: Adaptive segmentation of MRI data. TMI 15(4), 429–442 (1996)Google Scholar
- 18.Zhang, Y., et al.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. TMI 20(1), 45–57 (2001)Google Scholar