Toward the automatic quantification of in utero brain development in 3D structural MRI: A review

Benkarim, Oualid; Sanromà, Gerard; Zimmer, Veronika A.; Muñoz‐Moreno, Emma; Hahner, N.M.; Eixarch, Elisenda; Cámara, Óscar; Ballester, Miguel Ángel González; Piella, Gemma

doi:10.1002/hbm.23536

Cited by 39 publications

(27 citation statements)

References 107 publications

(285 reference statements)

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“…This result corresponds well to the asymmetric development of the fetal cortical surface. Several studies have reported the area of the right STS is larger and deeper than the left STS from 26 GW to 30 GW (Benkarim et al, ; Clouchoux, Du Plessis, et al, ; Dubois et al, ; Dubois & Dehaene‐Lambertz, ; Habas et al, ; Kasprian et al, ), and even in term‐born infants (Li et al, ). These results are in line with our findings where the region around the right STS is larger than the region around the left STS, and the growth rate of the right posterior temporal region is higher than the left posterior temporal region.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the medial occipital region of the fetal brain also exhibits an early asymmetry. Beginning at 24 GW, the parieto‐occipital sulcus is more concave on the right than the left hemisphere, and the difference becomes statistically significant after 26 GW (Benkarim et al, ; Habas et al, ). This is consistent with our finding that the right occipital and precuneus cluster is larger than the left occipital and precuneus cluster, and presents larger growth percentage than that on the left hemisphere.…”

Section: Discussionmentioning

confidence: 99%

“…First, the cortical folding patterns are actually highly variable across individuals (Duan et al, ; Meng et al, ) and poorly match with the boundaries defined by microstructure, function, and development (Zilles & Amunts, ). Second, primary and secondary cortical folds are not well established and are still developing rapidly in the fetal brain (Benkarim et al, ; Studholme, ), which makes the sulcal–gyral landmarks unstable. As shown in Figure , which illustrates the typical development of the fetal cortical surfaces, many sulcal–gyral folds do not exist in early gestation and are gradually emerging when growth proceeds.…”

Section: Introductionmentioning

confidence: 99%

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Fetal cortical surface atlas parcellation based on growth patterns

Xia

Wang

Benkarim

et al. 2019

Human Brain Mapping

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Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface‐based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data‐driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic “growth patterns” of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age‐specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fetal cortical surface atlas parcellation based on growth patterns

Xia

Wang

Benkarim

et al. 2019

Human Brain Mapping

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“…For example, segmentation is widely used as basic image quantification step in studies of early brain development (Benkarim et al, 2017) and dementia (Chupin et al, 2009; Li et al, 2007). …”

Section: Introductionmentioning

confidence: 99%

Learning non-linear patch embeddings with neural networks for label fusion

Sanromà

Benkarim

Piella

et al. 2018

Medical Image Analysis

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In brain structural segmentation, multi-atlas strategies are increasingly being used over single-atlas strategies because of their ability to fit a wider anatomical variability. Patch-based label fusion (PBLF) is a type of such multi-atlas approaches that labels each target point as a weighted combination of neighboring atlas labels, where atlas points with higher local similarity to the target contribute more strongly to label fusion. PBLF can be potentially improved by increasing the discriminative capabilities of the local image similarity measurements. We propose a framework to compute patch embeddings using neural networks so as to increase discriminative abilities of similarity-based weighted voting in PBLF. As particular cases, our framework includes embeddings with different complexities, namely, a simple scaling, an affine transformation, and non-linear transformations. We compare our method with state-of-the-art alternatives in whole hippocampus and hippocampal subfields segmentation experiments using publicly available datasets. Results show that even the simplest versions of our method outperform standard PBLF, thus evidencing the benefits of discriminative learning. More complex transformation models tended to achieve better results than simpler ones, obtaining a considerable increase in average Dice score compared to standard PBLF.

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“…First, sulcal-gyral folding patterns are actually poorly corresponding with the boundaries defined by microstructure, function and development [3]. Second, primary and secondary cortical folds are not well established and are still developing rapidly in the fetal brain [4, 5], as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Fetal cortical parcellation based on growth patterns

Xia

Zhang

Wang

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Dividing the human cerebral cortex into structurally and functionally distinct regions is important in many neuroimaging studies. Although many parcellations have been created for adults, they are not applicable for fetal studies, due to dramatic differences in brain size, shape and folding between adults and fetuses, as well as dynamic growth of fetal brains. To address this issue, we propose a novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures. As microstructures determine the molecular organization and functional principles of the cortex, growth patterns enable an accurate definition of distinct regions in development, microstructure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices and the other is based on the correlation profiles of vertices’ growth trajectories in relation to those of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better captures both their common and complementary information than by simply averaging them. Finally, based on this fused matrix, we perform spectral clustering to divide fetal cortical surfaces into distinct regions. We have applied our method on 25 normal fetuses from 26 to 29 gestational weeks and generated biologically meaningful parcellations.

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Toward the automatic quantification of in utero brain development in 3D structural MRI: A review

Cited by 39 publications

References 107 publications

Fetal cortical surface atlas parcellation based on growth patterns

Fetal cortical surface atlas parcellation based on growth patterns

Learning non-linear patch embeddings with neural networks for label fusion

Fetal cortical parcellation based on growth patterns

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