Brain Lesion Segmentation through Physical Model Estimation

Prastawa, Marcel; Gerig, Guido

doi:10.1007/978-3-540-89639-5_54

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…In part this is due to the increased difficulty due to the continuous evolution of the ischemic lesion in neonates and the unavailability of reliable anatomical templates of the developing brain. A range of strategies have been applied to measure lesional spatial regularization or congruity including MRI textures (13, 26), probabilistic models (28), morphological operations (29), and physical model estimation (30), most of which are computationally intensive and not useable for quick injury estimation.…”

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confidence: 99%

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Ghosh¹,

Recker²,

Shah³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To develop and compare an automated detection system for ischemic lesions in a neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO-H) from T2 weighted MRI (T2WI) to the currently used ''gold standard'' of manual segmentation.Materials and Methods: Forty-three P10 BCAO-H rat pups and 8 controls underwent T2WI at 1 day and 28 days. A computational imaging method, Hierarchical Region Splitting (HRS), was developed to automatically and rapidly detect and quantify 3D lesion and normal appearing brain matter (NABM) volumes.Results: HRS quantified lesion and NABM volumes within 15 s in comparison to 3 h for its manual counterpart, with a high correlation for injury (r 2 ¼ 0. 95; P ¼ 8.6 Â 10 À7 ) and NABM (r 2 ¼ 0. 92; P ¼ 1.4 Â 10 À22 ). Average lesion volumes for mild, moderate, and severe injuries were 3.85%, 28.85%, and 52.98% for HRS and 0.51%, 24.22%, and 48.74% for manual detection. Lesion volumes and locations were similar for both methods (sensitivity: 0.82, specificity: 0.86, and similarity: 1.47). Conclusion:HRS is an accurate, objective, and rapid method to quantify injury evolution in neonatal hypoxic ischemic injury models.

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confidence: 99%

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

Ghosh¹,

Recker²,

Shah³

et al. 2011

Magnetic Resonance Imaging

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“…Lesion detection can be improved by preprocessing the MR images using spatial regularization or congruity by MRI textures [ 41,42 ] , probabilistic models [ 24 ] , morphological operations [ 43 ] , and physical model estimation [ 44 ] . In some instances, multiple MRI modalities can be used sequentially to achieve better insights into the clinical relevance of the imaging abnormalities.…”

Section: Other Methodsmentioning

confidence: 99%

Computational Analysis: A Bridge to Translational Stroke Treatment

Ghosh

Sun

Turenius

et al. 2012

Translational Stroke Research

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Objective rapid quantifi cation of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specifi c treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fi elds (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the fi eld of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring of implanted neuronal stem cells in the ischemic brain, injury localization specifi c to different brain anatomical regions, and quantifi cation of stroke evolution, recovery and spatiotemporal interactions between injury volume/severity and treatment. Computational analysis is expected to open a new horizon in current clinical and translational stroke research by exploratory data mining that is not detectable using the standard "methods" of visual assessment of imaging data.

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“…statistical atlases (Warfield et al, 2000;Van Leemput et al, 2001), topological atlases (Shiee et al, 2010), disease-related rules (García-Lorenzo et al, 2011), physical models of lesion growth (Prastawa and Gerig, 2008), or healthy population intensity distributions (Roy et al, 2014;Tomas-Fernandez and Warfield, 2015).…”

Section: Accepted Manuscriptmentioning

confidence: 99%