Segmentation of the cortical plate in fetal brain MRI with a topological loss

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. In this paper, we propose for the first time the integration of a topological constraint, as an additional loss function, to enhance the morphological consistency of a deep learning-based segmentation of the fetal cortical plate. We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline method. Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Optimizing automated preprocessing streams for brain morphometric comparisons across multiple primate species

INTRODUCTION

MR techniques have delivered images of brains from a wide array of species, ranging from invertebrates to birds to elephants and whales. However, their potential to serve as a basis for comparative brain morphometric investigations has rarely been tapped so far (Christidis and Cox, 2006; Van Essen & Dierker, 2007), which also hampers a deeper understanding of the mechanisms behind structural alterations in neurodevelopmental disorders (Kochunov et al., 2010). One of the reasons for this is the lack of computational tools suitable for morphometrci comparisons across multiple species. In this work, we aim to characterize this gap, taking primates as an example.

METHODS

Using a legacy dataset comprising MR scans from eleven species of haplorhine primates acquired on the same scanner (Rilling & Insel, 1998), we tested different automated processing streams, focusing on denoising and brain segmentation. Newer multi-species datasets are not currently available, so our experiments with this decade-old dataset (which had a very low signal-to-noise ratio by contemporary standards) can serve to highlight the lower boundary of the current possibilities of automated processing pipelines. After manual orientation into Talairach space, an automated bias correction was performed using CARET (Van Essen et al., 2001) before the brains were extracted with FSL BET (Smith, 2002; Fig. 1) and either smoothed by an isotropic Gaussian Kernel, FSL SUSAN (Smith, 1996), an anisotropic diffusion filter (Perona & Malik, 1990), an optimized Rician non-local means filter (Gaser & Coupé, 2010), or not at all (Fig. 2 & 3). Segmentation of the brains (Fig. 2 & 4) was performed separately by either FSL FAST (Zhang, 2001) without atlas priors, or using an Adaptive Maximum A Posteriori Approach (Rajapakse et al., 1997). Finally, the white matter surface was extracted with CARET, and inspected for anatomical and topological correctness. 

RESULTS

Figure 3 shows that noise reduction was generally necessary but, at least for these noisy data, anisotropic filtering (SUSAN, diffusion filter, Rician filter) provided little improvement over simple isotropic filtering. While several segmentations worked well in individual species, our focus was on cross-species optimization of the processing pipeline, and none of the tested segmentations performed uniformly well in all 11 species. The performance could be improved by some of the denoising approaches and by deviating systematically from the default parameters recommended for processing human brains (cf. Fig. 4). Depending on the size of the brains and on the processing path, it took a double-core 2.4GHz iMac from about two minutes (squirrel monkeys) to half an hour (humans) to generate the white matter surface from the T1 image. Nonetheless, the resulting surfaces always necessitated topology correction and - often considerable - manual cleanup. 


CONCLUSIONS

Automated processing pipelines for surface-based morphometry still require considerable adaptations to reach optimal performance across brains of multiple species, even within primates (cf. Fig. 5). However, most contemporary datasets have a better signal-to-noise ratio than the ones used here, which provides for better segmentations and cortical surface reconstructions. Considering further that cross-scanner variability is well below within-species differences (Stonnington, 2008), the prospects look good for comparative evolutionary analyses of cortical parameters, and gyrification in particular. In order to succeed, however, computational efforts on comparative morphometry depend on high-quality imaging data from multiple species being more widely available.

ACKNOWLEDGMENTS

D.M, R.D, & C.G are supported by the German BMBF grant 01EV0709.


REFERENCES

Christidis, P & Cox, RW (2006), A Step-by-Step Guide to Cortical Surface Modeling of the Nonhuman Primate Brain Using FreeSurfer, Proc Human Brain Mapping Annual Meeting, http://afni.nimh.nih.gov/sscc/posters/file.2006-06-01.4536526043 .
Gaser, C & Coupé, P (2010), Impact of Non-local Means filtering on Brain Tissue Segmentation, OHBM 2010, Abstract 1770.
Kochunov, P & al. (2010), Mapping primary gyrogenesis during fetal development in primate brains: high-resolution in utero structural MRI study of fetal brain development in pregnant baboons, Frontiers in Neurogenesis, in press, DOI: 10.3389/fnins.2010.00020.
Perona, P & Malik J (1990), Scale space and edge detection using anisotropic diffusion, IEEE Trans Pattern Anal Machine Intell, vol. 12, no. 7, pp. 629-639.
Rajapakse, JC & al. (1997), Statistical approach to segmentation of single-channel cerebral MR images, IEEE Trans Med Imaging, vol. 16, no. 2, pp. 176-186.
Rilling, JK & Insel TR (1998), Evolution of the cerebellum in primates: differences in relative volume among monkeys, apes and humans. Brain Behav. Evol. 52, 308-314 doi:10.1159/000006575. Dataset available at http://www.fmridc.org/f/fmridc/77.html .
Smith, SM (1996), Flexible filter neighbourhood designation, Proc. 13th Int. Conf. on Pattern Recognition, vol. 1, pp. 206-212.
Smith, SM (2002), Fast robust automated brain extraction, Hum Brain Mapp, vol. 17, no. 3, pp. 143-155.
Stonnington, CM & al. (2008), Interpreting scan data acquired from multiple scanners: a study with Alzheimers disease, Neuroimage, vol. 39, no. 3, pp. 1180-1185.
Van Essen, DC & al. (2001), An Integrated Software System for Surface-based Analyses of Cerebral Cortex, J Am Med Inform Assoc, vol. 8, no. 5, pp. 443-459.
Van Essen, DC & Dierker DL (2007), Surface-based and probabilistic atlases of primate cerebral cortex, Neuron, vol. 56, no. 2, pp. 209-225.
Zhang, Y & al. (2001), Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm, IEEE Trans Med Imaging, vol. 20, no. 1, pp. 45-57.
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Quantification of cortical folding using MR image data

The cerebral cortex is a thin layer of tissue lining the brain where neural circuits perform important high level functions including sensory perception, motor control and language processing. In the third trimester the fetal cortex folds rapidly from a smooth sheet into a highly convoluted arrangement of gyri and sulci. Premature birth is a high risk factor for poor neurodevelopmental outcome and has been associated with abnormal cortical development, however the nature of the disruption to developmental processes is not fully understood. Recent developments in magnetic resonance imaging have allowed the acquisition of high quality brain images of preterms and also fetuses in-utero. The aim of this thesis is to develop techniques which quantify folding from these images in order to better understand cortical development in these two populations. A framework is presented that quantifies global and regional folding using curvature-based measures. This methodology was applied to fetuses over a wide gestational age range (21.7 to 38.9 weeks) for a large number of subjects (N = 80) extending our understanding of how the cortex folds through this critical developmental period. The changing relationship between the folding measures and gestational age was modelled with a Gompertz function which allowed an accurate prediction of physiological age. A spectral-based method is outlined for constructing a spatio-temporal surface atlas (a sequence of mean cortical surface meshes for weekly intervals). A key advantage of this method is the ability to do group-wise atlasing without bias to the anatomy of an initial reference subject. Mean surface templates were constructed for both fetuses and preterms allowing a preliminary comparison of mean cortical shape over the postmenstrual age range 28-36 weeks. Displacement patterns were revealed which intensified with increasing prematurity, however more work is needed to evaluate the reliability of these findings.Open Acces

Changes in the Frontotemporal Cortex and Cognitive Correlates in First-Episode Psychosis

Background: Loss of cortical volume in frontotemporal regions has been reported in patients with schizophrenia and their relatives. Cortical area and thickness are determined by different genetic processes, and measuring these parameters separately may clarify disturbances in corticogenesis relevant to schizophrenia. Our study also explored clinical and cognitive correlates of these parameters.Methods: Thirty-seven patients with first-episode psychosis (34 schizophrenia, 3 schizoaffective disorder) and 38 healthy control subjects matched for age and sex took part in the study. Imaging was performed on an magnetic resonance imaging 1.5-T scanner. Area and thickness of the frontotemporal cortex were measured using a surface-based morphometry method (Freesurfer). All subjects underwent neuropsychologic testing that included measures of premorbid and current IQ, working and verbal memory, and executive function.Results: Reductions in cortical area, more marked in the temporal cortex, were present in patients. Overall frontotemporal cortical thickness did not differ between groups, although regional thinning of the right superior temporal region was observed in patients. There was a significant association of both premorbid IQ and IQ at disease onset with area, but not thickness, of the frontotemporal cortex, and working memory span was associated with area of the frontal cortex. These associations remained significant when only patients with schizophrenia were considered.Conclusions: Our results suggest an early disruption of corticogenesis in schizophrenia, although the effect of subsequent environmental factors cannot be excluded. In addition, cortical abnormalities are subject to regional variations and differ from those present in neurodegenerative diseases

Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration

Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditional deep learning-based automatic segmentation, although effective, requires extensive training data with ground-truth labels, typically produced by clinicians through a time-consuming annotation process. To overcome this challenge, we propose a novel unsupervised segmentation method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image. This cascaded network can then be used to register multiple annotated images with the image to be segmented, and combine the propagated labels to form a refined segmentation. Our experiments demonstrate that the proposed cascaded architecture outperforms the state-of-the-art registration methods that were tested. Furthermore, the derived segmentation method achieves similar performance and inference time to nnU-Net while only using a small subset of annotated data for the multi-atlas segmentation task and none for training the network. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.Comment: 17 pages, 8 figures, 5 tables, paper submitted to IEEE transaction on medical imagin

Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome.

How does human brain structure mature during adolescence? We used MRI to measure cortical thickness and intracortical myelination in 297 population volunteers aged 14-24 y old. We found and replicated that association cortical areas were thicker and less myelinated than primary cortical areas at 14 y. However, association cortex had faster rates of shrinkage and myelination over the course of adolescence. Age-related increases in cortical myelination were maximized approximately at the internal layer of projection neurons. Adolescent cortical myelination and shrinkage were coupled and specifically associated with a dorsoventrally patterned gene expression profile enriched for synaptic, oligodendroglial- and schizophrenia-related genes. Topologically efficient and biologically expensive hubs of the brain anatomical network had greater rates of shrinkage/myelination and were associated with overexpression of the same transcriptional profile as cortical consolidation. We conclude that normative human brain maturation involves a genetically patterned process of consolidating anatomical network hubs. We argue that developmental variation of this consolidation process may be relevant both to normal cognitive and behavioral changes and the high incidence of schizophrenia during human brain adolescence.This study was supported by the Neuroscience in Psychiatry Network, a strategic award by the Wellcome Trust to the University of Cambridge and University College London. Additional support was provided by the NIHR Cambridge Biomedical Research Centre and the MRC/Wellcome Trust Behavioural & Clinical Neuroscience Institute. PEV is supported by the MRC (MR/K020706/1). We used the Darwin Supercomputer of the University of Cambridge High Performance Computing Service provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council.This is the author accepted manuscript. This is the author accepted manuscript. The final version is available from the National Academy of Sciences via https://doi.org/10.1073/pnas.160174511