Zhengwang Wu

Computational neuroanatomy of baby brains: A review

&NA; The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non‐invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within‐tissue intensity variations, and regionally‐heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant‐tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state‐of‐the‐art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant‐dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions. Graphical abstract T1w, T2w, FA images, tissue segmentation results as well as the reconstructed inner and outer surfaces of a typically‐developing infant, scanned longitudinally at 2 weeks, 3, 6, 9 and 12 months of age. Inner surfaces are color‐coded with the maximum principal curvature, and outer surfaces are color‐coded with cortical thickness. Figure. No caption available. HighlightsA comprehensive review of infant‐dedicated computational methods and tools.A discussion of contributions to the understanding of infant brain development.A discussion on current limitations and potential future directions.

Segmenting hippocampal subfields from 3T MRI with multi-modality images

HighlightsUsing multi‐modality MRI for hippocampal subfields segmentation.First explore to capture the connectivity pattern from the resting‐state fMRI for hippocampal subfields segmentation.A learning‐based automatic hippocampal subfields segmentation method.Features from multi‐modality MRI could lead to boosted hippocampal subfields segmentation performance. Graphical abstract Figure. No Caption available. Abstract Hippocampal subfields play important roles in many brain activities. However, due to the small structural size, low signal contrast, and insufficient image resolution of 3T MR, automatic hippocampal subfields segmentation is less explored. In this paper, we propose an automatic learning‐based hippocampal subfields segmentation method using 3T multi‐modality MR images, including structural MRI (T1, T2) and resting state fMRI (rs‐fMRI). The appearance features and relationship features are both extracted to capture the appearance patterns in structural MR images and also the connectivity patterns in rs‐fMRI, respectively. In the training stage, these extracted features are adopted to train a structured random forest classifier, which is further iteratively refined in an auto‐context model by adopting the context features and the updated relationship features. In the testing stage, the extracted features are fed into the trained classifiers to predict the segmentation for each hippocampal subfield, and the predicted segmentation is iteratively refined by the trained auto‐context model. To our best knowledge, this is the first work that addresses the challenging automatic hippocampal subfields segmentation using relationship features from rs‐fMRI, which is designed to capture the connectivity patterns of different hippocampal subfields. The proposed method is validated on two datasets and the segmentation results are quantitatively compared with manual labels using the leave‐one‐out strategy, which shows the effectiveness of our method. From experiments, we find a) multi‐modality features can significantly increase subfields segmentation performance compared to those only using one modality; b) automatic segmentation results using 3T multi‐modality MR images could be partially comparable to those using 7T T1 MRI.

4D Infant Cortical Surface Atlas Construction Using Spherical Patch-Based Sparse Representation

The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first, we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness. Second, instead of simply averaging over the co-registered surfaces, a spherical patch-based sparse representation is developed to overcome possible surface registration errors across different subjects. The central idea is that, for each local spherical patch in the atlas space, we build a dictionary, which includes the samples of current local patches and their spatially-neighboring patches of all co-registered surfaces, and then the current local patch in the atlas is sparsely represented using the built dictionary. Compared to the atlas built with the conventional methods, the 4D infant cortical surface atlas constructed by our method preserves more details of cortical folding patterns, thus leading to boosted accuracy in registration of new infant cortical surfaces.

Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs

This paper proposes a novel method of using regression-guided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxels deformation to the nearest point on the ROI boundary as well as each voxels class label (e.g., ROI versus background). The auto-context model further refines all voxels deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.

Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images

Hippocampal subfields play important and divergent roles in both memory formation and early diagnosis of many neurological diseases, but automatic subfield segmentation is less explored due to its small size and poor image contrast. In this paper, we propose an automatic learning-based hippocampal subfields segmentation framework using multi-modality 3TMR images, including T1 MRI and resting-state fMRI (rs-fMRI). To do this, we first acquire both 3T and 7T T1 MRIs for each training subject, and then the 7T T1 MRI are linearly registered onto the 3T T1 MRI. Six hippocampal subfields are manually labeled on the aligned 7T T1 MRI, which has the 7T image contrast but sits in the 3T T1 space. Next, corresponding appearance and relationship features from both 3T T1 MRI and rs-fMRI are extracted to train a structured random forest as a multi-label classifier to conduct the segmentation. Finally, the subfield segmentation is further refined iteratively by additional context features and updated relationship features. To our knowledge, this is the first work that addresses the challenging automatic hippocampal subfields segmentation using 3T routine T1 MRI and rs-fMRI. The quantitative comparison between our results and manual ground truth demonstrates the effectiveness of our method. Besides, we also find that (a) multi-modality features significantly improved subfield segmentation performance due to the complementary information among modalities; (b) automatic segmentation results using 3T multimodality images are partially comparable to those on 7T T1 MRI.

Registration-Free Infant Cortical Surface Parcellation Using Deep Convolutional Neural Networks.

Automatic parcellation of infant cortical surfaces into anatomical regions of interest (ROIs) is of great importance in brain structural and functional analysis. Conventional cortical surface parcellation methods suffer from two main issues: 1) Cortical surface registration is needed for establishing the atlas-to-individual correspondences; 2) The mapping from cortical shape to the parcellation labels requires designing of specific hand-crafted features. To address these issues, in this paper, we propose a novel cortical surface parcellation method, which is free of surface registration and designing of hand-crafted features, based on deep convolutional neural network (DCNN). Our main idea is to formulate surface parcellation as a patch-wise classification problem. Briefly, we use DCNN to train a classifier, whose inputs are the local cortical surface patches with multi-channel cortical shape descriptors such as mean curvature, sulcal depth, and average convexity; while the outputs are the parcellation label probabilities of cortical vertices. To enable effective convolutional operation on the surface data, we project each spherical surface patch onto its intrinsic tangent plane by a geodesic-distance-preserving mapping. Then, after classification, we further adopt the graph cuts method to improve spatial consistency of the parcellation. We have validated our method based on 90 neonatal cortical surfaces with manual parcellations, showing superior accuracy and efficiency of our proposed method.

Infant brain development prediction with latent partial multi-view representation learning

The early postnatal period witnesses rapid and dynamic brain development. Understanding the cognitive development patterns can help identify various disorders at early ages of life and is essential for the health and well-being of children. This inspires us to investigate the relation between cognitive ability and the cerebral cortex by exploiting brain images in a longitudinal study. Specifically, we aim to predict the infant brain development status based on the morphological features of the cerebral cortex. For this goal, we introduce a multi-view multi-task learning approach to dexterously explore complementary information from different time points and handle the missing data simultaneously. Specifically, we establish a novel model termed as Latent Partial Multi-view Representation Learning. The approach regards data of different time points as different views, and constructs a latent representation to capture the complementary underlying information from different and even incomplete time points. It uncovers the latent representation that can be jointly used to learn the prediction model. This formulation elegantly explores the complementarity, effectively reduces the redundancy of different views, and improves the accuracy of prediction. The minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on real data validate the proposed method.

A computational method for longitudinal mapping of orientation-specific expansion of cortical surface area in infants

The dynamic expansion of the human cortical surface during infancy is largely driven by the increase of surface area in two orthogonal directions: 1) the expansion parallel to the folding orientation (i.e., increasing the lengths of folds) and 2) the expansion perpendicular to the folding orientation (i.e., increasing the depths of folds). The knowledge on this would help us better understand the cortical growth mechanisms and provide important insights into neurodevelopmental disorders, but still remains scarce, due to the lack of dedicated computational methods. To address this issue, we propose a novel method for longitudinal mapping of orientation-specific expansion of cortical surface area in these two orthogonal directions during early infancy. We apply our method to30 healthy infants, and for the first time reveal the orientation-specific longitudinal cortical surface expansion maps during the first postnatal year.

Construction of spatiotemporal neonatal cortical surface atlases using a large-scale dataset

The cortical surface atlases constructed from a large representative population of neonates are highly needed in the neonatal neuroimaging studies. However, existing neonatal cortical surface atlases are typically constructed from small datasets, e.g., tens of subjects, which are inherently biased and thus are not representative to the neonatal population. In this paper, we construct neonatal cortical surface atlases based on a large-scale dataset with 764 subjects. To better characterize the dynamic cortical development during the first postnatal weeks, instead of constructing just a single atlas, we construct a set of spatiotemporal atlases at each week from 39 to 44 gestational weeks. The central idea is that, for all cortical surfaces, we first group-wisely register them into the common space to ensure the unbiasedness. Then, rather than simply averaging over the co-registered cortical surfaces, which generally leads to over-smoothed cortical folding patterns, we adopt a spherical patch-based sparse representation using an augmented dictionary to overcome the noises and potential registration errors. Through the group-wise sparsity constraint, we obtain consistent geometric cortical folding attributes on the atlases. Our atlases preserve the sharp cortical folding patterns, thus leading to better registration accuracy when aligning new subjects onto the atlases.

Construction of spatiotemporal infant cortical surface atlas of rhesus macaque

As a widely used animal model in MR imaging studies, rhesus macaque helps to better understand both normal and abnormal neural development in the human brain. However, the available adult macaque brain atlases are not well suitable for study of brain development at the early postnatal stage, since this stage undergoes dramatic changes in brain appearances and structures. Building age matched atlases for this critical period is thus highly desirable yet still lacking. In this paper, we construct the first spatiotemporal (4D) cortical surface atlases for rhesus macaques from 2 weeks to 24 months, using 138 longitudinal MRI scans from 32 healthy rhesus monkeys. Specifically, we first perform intra-subject cortical surface registration to obtain within-subject mean cortical surfaces. Then, we perform inter-subject registration of within-subject mean surfaces to obtain unbiased and longitudinally-consistent 4D cortical surface atlases. Based on our 4D rhesus monkey atlases, we further chart the first developmental-trajectories-based parcellation maps using the local surface area and spectral clustering algorithm. Our 4D macaque surface atlases and parcellation maps will greatly facilitate early brain development studies of macaques.