Gaussian mixture model-based segmentation of MR images taken from premature infant brains

Merisaari, Harri; Parkkola, Riitta; Alhoniemi, Esa; Teräs, Mika; Lehtonen, Liisa; Haataja, Leena; Lapinleimu, Helena; Nevalainen, Olli

doi:10.1016/j.jneumeth.2009.05.026

Cited by 23 publications

(12 citation statements)

References 19 publications

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“…Recently there has been an increasing use of T 1 -W and T 2 -W images for brain segmentation in preterm and term neonates [30][31][32][33][34][35][36][37]. Moreover, acquiring volumetric sequences allows for retrospective analyses on the neonates scanned, by having a standardized data set for comparison with future MR studies on the same child.…”

Section: Discussionmentioning

confidence: 99%

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Optimized T1- and T2-weighted volumetric brain imaging as a diagnostic tool in very preterm neonates

et al. 2010

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Background T1-and T 2 -W MR sequences used for obtaining diagnostic information and morphometric measurements in the neonatal brain are frequently acquired using different imaging protocols. Optimizing one protocol for obtaining both kinds of information is valuable. Objective To determine whether high-resolution T1-and T 2 -W volumetric sequences optimized for preterm brain imaging could provide both diagnostic and morphometric value. Materials and methods Thirty preterm neonates born between 24 and 32 weeks' gestational age were scanned during the first 2 weeks after birth. T1-and T 2 -W highresolution sequences were optimized in terms of signal-tonoise ratio, contrast-to-noise ratio and scan time and compared to conventional spin-echo-based sequences. Results No differences were found between conventional and high-resolution T 1 -W sequences for diagnostic confidence, image quality and motion artifacts. A preference for conventional over high-resolution T 2 -W sequences for image quality was observed. High-resolution T1 images provided better delineation of thalamic myelination and the superior temporal sulcus. No differences were found for detection of myelination and sulcation using conventional and high-resolution T 2 -W images. Conclusion High-resolution T1-and T 2 -W volumetric sequences can be used in clinical MRI in the very preterm brain to provide both diagnostic and morphometric information.R. Nossin-Manor (*)

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Section: Discussionmentioning

confidence: 99%

“…These images are also used for segmentation and quantitative morphometric measurements in the developing brain [14,17,[30][31][32][33][34][35][36][37]. Frequently, however, sequences used for clinical diagnosis and morphometric measurements are acquired using different imaging protocols and experimental settings.…”

Section: Introductionmentioning

confidence: 99%

Optimized T1- and T2-weighted volumetric brain imaging as a diagnostic tool in very preterm neonates

et al. 2010

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“…A method that uses watershed segmentation, Gaussian mixture model clustering and a modification of BET is employed [71] to segment MR images of premature infant brains. Tao and Chang [72] developed a deformable surfacebased algorithm that first analyzes the intensity of the entire image to find an approximate centroid of the brain and then it initializes an ellipsoidal surface around it.…”

Section: Deformable Surface-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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“…In the past several years, many efforts were put into neonatal brain MRI segmentation (Anbeek et al, 2008; Gui et al, 2012; Leroy et al, 2011; Merisaari et al, 2009; Wang et al, 2011; Wang et al, 2012; Wang et al, 2013c; Warfield et al, 2000; Xue et al, 2007), prompted by the increasing availability of neonatal images. Most proposed methods are atlas-based (Cocosco et al, 2003; Prastawa et al, 2005; Shi et al, 2009; Shi et al, 2011a; Song et al, 2007; Warfield et al, 2000; Weisenfeld et al, 2006; Weisenfeld and Warfield, 2009).…”

Section: Introductionmentioning

confidence: 99%

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

et al. 2015

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Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy.

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Gaussian mixture model-based segmentation of MR images taken from premature infant brains

Cited by 23 publications

References 19 publications

Optimized T1- and T2-weighted volumetric brain imaging as a diagnostic tool in very preterm neonates

Optimized T1- and T2-weighted volumetric brain imaging as a diagnostic tool in very preterm neonates

Methods on Skull Stripping of MRI Head Scan Images—a Review

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

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