Lesion identification using unified segmentation-normalisation models and fuzzy clustering

By Mohamed L. Seghier, Anil F. Ramlackhansingh, Jennifer T. Crinion, Alexander P. Leff, Cathy J. Price. NeuroImage, 2008

344 citations recorded by the source corpus.

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.

Subfields: Computer Vision and Pattern Recognition, Neurology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/d3260d0b4a042fda08d562c17b61e6c0f4a35df9

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, background): “Finally, a large variety of imaging modalities can be used for mapping tumor-induced tissue changes, including T2 and FLAIR MRI (highlighting differences in tissue water relaxational properties), post-Gadolinium T1 MRI (showing pathological intratumoral take-up of contrast agents), perfusion and…”
The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke (2009, unknown): “…a modified version of the tissue segmentation component has been developed to further improve identification and spatial normalization of ‘brain’ as opposed to ‘non-brain’ components by adding in an extra tissue class, ‘lesion’, into which outlier voxels can be classified (Seghier et al., 2008).”
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI (2016, background): “139 ub-140 In-108 formation about the stroke evolution phase is sometimes omit-109 ted (Seghier et al., 2008; Forbes et al., 2010) or, if mentioned, not clearly deﬁned (Saad et al., 2011; Muda et al., 2015).”
Brain templates and atlases (2012, methodology): “Thompson et al. (1998) and Oishi et al. (2009) have described and employed atlases of structural deformations in Alzheimer's Disease, while numerous stroke atlases have been built and used for classification (Bilello et al., 2006; Nowinski et al., 2006; Seghier et al., 2008; Shen et al., 2010).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “[137] xtended the tissue segmentation approach of Ashburner and riston [114] to detect stroke lesions.”