Integrated graph cuts for brain MRI segmentation
Zhuang Song,Nicholas J. Tustison,Brian B. Avants,James C. Gee +3 more
- Vol. 9, pp 831-838
TLDR
A new method of information integration in a graph based framework where tissue priors and local boundary information are integrated into the edge weight metrics in the graph and inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneous estimation.Abstract:
Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.read more
Citations
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Journal ArticleDOI
Fast and robust multi-atlas segmentation of brain magnetic resonance images.
Jyrki Lötjönen,Robin Wolz,Juha Koikkalainen,Lennart Thurfjell,Gunhild Waldemar,Hilkka Soininen,Daniel Rueckert +6 more
TL;DR: An optimised pipeline for multi-atlas brain MRI segmentation is introduced and intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time.
Journal ArticleDOI
White matter lesion extension to automatic brain tissue segmentation on MRI
Renske de Boer,Henri A. Vrooman,Fedde van der Lijn,Meike W. Vernooij,M. Arfan Ikram,Aad van der Lugt,Monique M.B. Breteler,Wiro J. Niessen,Wiro J. Niessen +8 more
TL;DR: A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation, indicating that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.
Journal ArticleDOI
Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation
TL;DR: A general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlasRegistration and patch-based segmentation, two widely used methods in brain segmentation.
Journal ArticleDOI
LEAP: Learning embeddings for atlas propagation
TL;DR: An increasing gain in accuracy is demonstrated of the new method, compared to standard multi-atlas segmentation, with increasing distance between the target image and the initial set of atlases in the coordinate embedding, i.e., with a greater difference between atlas and image.
Journal ArticleDOI
Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts
TL;DR: Direct quantitative and qualitative comparisons showed that the proposed method outperforms a multi-atlas based segmentation method and shows significant associations with cognitive decline and dementia, similar to the manually measured volumes.
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