An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior

Hwang, Jinyoung; Kim, Junmo; Han, Youngnam; Park, HyunWook

doi:10.1016/j.mri.2011.01.005

Cited by 21 publications

(24 citation statements)

References 20 publications

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“…A better result can be achieved by directly carrying out a cerebellum segmentation step. Several approaches for whole cerebellum segmentation have been proposed [16, 22]. To differentiate cerebellum from non-cerebellum, these methods typically use intensity and texture information together with prior information about the position and shape of the cerebellum.…”

Section: Methodsmentioning

confidence: 99%

“…These methods typically provide a whole cerebellum segmentation and also segment its gray matter (GM) and white matter (WM). Several specialized methods for segmenting just the cerebellum itself have also been developed; one method uses atlas registration and local image descriptors [29] and another uses an active contour model with a shape prior [22]. …”

Section: Introductionmentioning

confidence: 99%

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Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease

Yang

Bogovic

et al. 2016

NeuroImage

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The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes show distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease

Yang

Bogovic

et al. 2016

NeuroImage

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“…This methodology has led to advances in adaptive face recognition systems [41], and has shown great promise in difficult medical imaging segmentation problems [42]. Unlike intensity-only boundary segmentation or energy minimizing curves (active contours [43, 44]), AAMs encapsulate variation from real examples and restrict the search space to plausible combinations. Since the variability of cerebellar appearance tends to be closely distributed, the use of contextual knowledge results in reliable estimation and sensible transitions between parameters.…”

Section: Methodsmentioning

confidence: 99%

Automated MRI cerebellar size measurements using active appearance modeling

2014

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Although the human cerebellum has been increasingly identified as an important hub that shows potential for helping in the diagnosis of a large spectrum of disorders, such as alcoholism, autism, and fetal alcohol spectrum disorder, the high costs associated with manual segmentation, and low availability of reliable automated cerebellar segmentation tools, has resulted in a limited focus on cerebellar measurement in human neuroimaging studies. We present here the CATK (Cerebellar Analysis Toolkit), which is based on the Bayesian framework implemented in FMRIB’s FIRST. This approach involves training Active Appearance Models (AAM) using hand-delineated examples. CATK can currently delineate the cerebellar hemispheres and three vermal groups (lobules I–V, VI–VII, and VIII–X). Linear registration with the low-resolution MNI152 template is used to provide initial alignment, and Point Distribution Models (PDM) are parameterized using stellar sampling. The Bayesian approach models the relationship between shape and texture through computation of conditionals in the training set. Our method varies from the FIRST framework in that initial fitting is driven by 1D intensity profile matching, and the conditional likelihood function is subsequently used to refine fitting. The method was developed using T1-weighted images from 63 subjects that were imaged and manually labeled: 43 subjects were scanned once and were used for training models, and 20 subjects were imaged twice (with manual labeling applied to both runs) and used to assess reliability and validity. Intraclass correlation analysis shows that CATK is highly reliable (average test-retest ICCs of 0.96), and offers excellent agreement with the gold standard (average validity ICC of 0.87 against manual labels). Comparisons against an alternative atlas-based approach, SUIT (Spatially Unbiased Infratentorial Template), that registers images with a high-resolution template of the cerebellum, show that our AAM approach offers superior reliability and validity. Extensions of CATK to cerebellar hemisphere parcels is envisioned.

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“…The technique has been shown to be comparable in accuracy to manual labeling and reliable and robust across sessions, scanner platforms, updates and field strengths (Han & Fischl, 2007;Jovicich et al, 2009). Some studies have also shown the robust and accurate segmentation results on cerebellar analysis (Hwang, Kim, Han, & Park, 2011;Weier et al, 2012).…”

Section: Data Acquisition and Analysismentioning

confidence: 99%