A Hybrid Approach to Brain Extraction from Premature Infant MRI

Péporté, Michèle; Ghita, Dana E. Ilea; Twomey, Eilish; Whelan, Paul F.

doi:10.1007/978-3-642-21227-7_67

Cited by 13 publications

(14 citation statements)

References 28 publications

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“…Nevertheless most algorithms have reported good results on par or better than previously published works. In particular, Daliri et al [29] have shown an improvement in SI values compared to Ghadimi et al [36]; Peporte et al [71] have also reported better performance of their approach compared to four publicly available brain extraction tools, namely, BrainSuite 6 , SPM8-Statistical Parametric Mapping 7 , FMRIB Software Library (FSL) 8 and MRIcroN 9 . Likewise, Mahapatra [58] has demonstrated better accuracy of their results in relation to the graph cut approach of Sadananthan et al [83] and other publicly available software like BET [97], BSE [89] and Hybrid Watershed Algorithm (HWA) [86] that is a part of FreeSurfer Software Suite 10 .…”

Section: Validation Of Resultsmentioning

confidence: 94%

“…Ghadimi et al [36] Similarity index [29] Similarity index Peporte et al [71] Dice metric, Jaccard metric, false positive, false negative Mahapatra [58] Dice metric, Jaccard index, sensitivity, specificity, false positive rate and Hausdorff distance 4.2.2.1. Atlas-based algorithms.…”

Section: Authors Validation Metricsmentioning

confidence: 99%

“…Prior shape and gradient information were integrated with graph cut based optimization for skull stripping the neonatal images. Conversely, an atlas-free skull stripping approach was advocated by Peporte et al [71]. It involved the creation of a binary mask, region of interest (ROI) segmentation by region growing and a final edge refinement.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

“…at preterm gestation is different from scanning the preterm baby at its term equivalent age (TEA). Since different segmentation algorithms in the literature employed locally available neonatal datasets, some approaches utilized images of preterm brains scanned at term (Peporte, 2011;Egekher, 2013) whereas few others included images of preterm brains scanned at preterm gestation as well as at term (Makropoulos, 2012;Melbourne, 2012); yet others worked only on brain images of term neonates [100].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neonatal brain MRI segmentation: A review

Devi¹,

Chandrasekharan²,

Sundararaman³

et al. 2015

Computers in Biology and Medicine

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Section: Validation Of Resultsmentioning

confidence: 94%

Section: Authors Validation Metricsmentioning

confidence: 99%

Section: Segmentation Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neonatal brain MRI segmentation: A review

Devi¹,

Chandrasekharan²,

Sundararaman³

et al. 2015

Computers in Biology and Medicine

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“…Fuzzy rules were used to define the positions of the triangles. Peporte et al [23] Presented an approach called Hybrid Skull-Stripping (HSS). A binary mask was generated using morphological operators to define the brain tissue outer boundary, which was later followed by region growing and edge detection.…”

Section: Introductionmentioning

confidence: 99%

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

Alansary

Ismail

Soliman

et al. 2016

IEEE J. Biomed. Health Inform.

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In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.

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Hierarchical Approach for Neonate Cerebellum Segmentation from MRI: An Experimental Study

Cettour-Janet

Valette

Lucas

et al. 2019

Lecture Notes in Computer Science

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Morphometric analysis of brain structures is of high interest for premature neonates, in particular for defining predictive neurodevelopment biomarkers. This requires beforehand, the correct segmentation of structures of interest from MR images. Such segmentation is however complex, due to the resolution and properties of data. In this context, we investigate the potential of hierarchical image models, and more precisely the binary partition tree, as a way of developing efficient, interactive and user-friendly 3D segmentation methods. In particular, we experiment the relevance of texture features for defining the hierarchy of partitions constituting the final segmentation space. This is one of the first uses of binary partition trees for 3D segmentation of medical images. Experiments are carried out on 19 MR images for cerebellum segmentation purpose.

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A Hybrid Approach to Brain Extraction from Premature Infant MRI

Cited by 13 publications

References 28 publications

Neonatal brain MRI segmentation: A review

Neonatal brain MRI segmentation: A review

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

Hierarchical Approach for Neonate Cerebellum Segmentation from MRI: An Experimental Study

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