Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy

Noorizadeh, Negar; Kazemi, Kamran; Danyali, Habibollah; Aarabi, Ardalan

doi:10.1016/j.bspc.2019.101602

Cited by 8 publications

(2 citation statements)

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“…This low performance is expected and comprehensible for the methods which use low-level image processing tools (Burguet et al, 2004; Despotovic et al, 2009; Yamaguchi et al, 2010), because they rely on image intensities for discerning between tissues whereas the skull is quasi-imperceptible in neonatal MR images. Artificial intelligence-based and atlas-based methods (Daliri et al, 2010; Mahapatra, 2012; Noorizadeh et al, 2019; Shi et al, 2012) on the other hand yielded unsatisfactory results, mainly due to imperfection of their skull geometry knowledge-base.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the skull removal from pediatric MR images, Shi et al (Shi et al, 2012) developed a brain-extraction meta-algorithm and integrated it into the analysis and neonatal brain extraction toolbox (iBEAT). Noorizadeh et al (Noorizadeh et al, 2019) presented a multi-atlas patch-based label fusion method for automatic brain extraction from T2-weighted neonatal MR images. Most of the above methods suffer from unrealistic and low accuracy rates of neonatal skull segmentation results.…”

Section: Introductionmentioning

confidence: 99%

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Skull and scalp segmentation in neonatal cerebral MRI using subject-specific probability models

Hokmabadi

Moghaddam

Mohtasebi

et al. 2022

Preprint

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This study presents a new approach for the segmentation of cranial bones in magnetic resonance images (MRIs) acquired from neonates in the age range of 39 to 42 weeks, gestational age. This approach uses subject specific probability maps of the skull and scalp, which are created from atlas computed tomography (CT) images taken retrospectively from neonates in the same age range. Our method uses also a subject specific probability map of cerebrospinal fluid (CSF), which is constructed from retrospective atlas MRIs. To construct skull, scalp and CSF probability maps, a subject specific bimodal MR-CT neonatal head template is created (using atlas MR and CT images), and employed. In the next step, the subject specific probability maps are fed to expectation maximization algorithm in conjunction with Markov random field method implemented in FSL software to segment skull and scalp from the input MR image. The results of the proposed method were evaluated through various experiments. First, we computed the similarity between frontal and occipital sutures (reconstructed from segmented cranial bones) and the ground truth (created manually by an expert radiologist). For this purpose, modified versions of Dice similarity index (DSI) were adopted and used. Second, the size of anterior fontanel was compared to its normal size as reported for the neonates in the same age range. Third, the thickness of cranial bones was computed and compared to its normal values as reported for healthy neonates. Finally, a retrospective data including MRI and CT images was used which have been acquired from the same neonate within a short time interval. After aligning the two images, the similarity between cranial bones of the MR and CT image was compared using DSI and modified Hausdorff distance. The results of these experiments demonstrated the success of our segmentation method.

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