Atlas-based segmentation of the germinal matrix from in utero clinical MRI of the fetal brain

The nnU-Net based method for automatic segmenting fetal brain tissues

The magnetic resonance (MR) images of fetuses make it possible for doctors to detect out pathological fetal brains in early stages. Brain tissue segmentation is prerequisite for making brain morphology and volume analyses. nnU-Net is an automatic segmentation method based on deep learning. It can adaptively configure itself, so as to adapt to a specific task via preprocessing, network architecture, training, and post-processing. Therefore, we adapt nnU-Net to segment seven types of fetal brain tissues, including external cerebrospinal fluid, gray matter, white matter, ventricle, cerebellum, deep gray matter, and brainstem. With regard to the characteristics of the FeTA 2021 data, some adjustments are made to the original nnU-Net, so that it can segment seven types of fetal brain tissues precisely as far as possible. The average segmentation results on FeTA 2021 training data demonstrate that our advanced nnU-Net is superior to the peers including SegNet, CoTr, AC U-Net and ResUnet. Its average segmentation results are 0.842, 11.759 and 0.957 in terms of Dice, HD95 and VS criteria. Moreover, the experimental results on FeTA 2021 test data further demonstrate that our advanced nnU-Net has obtained good segmentation performance of 0.774, 14.699 and 0.875 in terms of Dice, HD95 and VS, ranked the third in FeTA 2021 challenge. Our advanced nnU-Net realized the task for segmenting the fetal brain tissues using MR images of different gestational ages, which can help doctors to make correct and seasonable diagnoses.

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.

A new approach to automatic fetal brain extraction from MRI using a variational level set method

BACKGROUND AND PURPOSE

Appropriate images extracted from the MRI of mothers' wombs can be of great help in the medical diagnosis of fetal abnormalities. As maternal tissue may appear in such images, affecting visualization of myelination of the fetal brain, it is not possible to use methods routinely used for extraction of adult brains for fetal brains. The aim of the present study was to use a variational level set approach to extract fetal brain from T2-weighted MR images of the womb.

METHODS

Coronal T2-weighted images were acquired using fast MRI protocols (to avoid artifacts). The database includes 105 MR images from eight subjects. After correcting the inhomogeneity of the images, the fetal eyes were located, and from that information, the location of the fetus brain was automatically determined. Then, the variational level set was used for fetus brain extraction. The results were analyzed by a clinical specialist (radiologist) and the similarity (Dice and Jaccard coefficients), sensitivity and specificity were calculated.

RESULTS AND CONCLUSIONS

The means of the statistical analysis for the Dice and Jaccard coefficients, sensitivity and specificity, were 99.56%, 96.89%, 95.71%, and 97.96%, respectively. Thus, extraction of fetal brain from MR images was confirmed, both statistically and visually through cross-validation.

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

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.

Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future.

Quantifying and modelling tissue maturation in the living human fetal brain

Recent advances in medical imaging are beginning to allow us to quantify brain tissue maturation in the growing human brain prior to normal term age, and are beginning to shed new light on early human brain growth. These advances compliment the work already done in cellular level imaging in animal and post mortem studies of brain development. The opportunities for collaborative research that bridges the gap between macroscopic and microscopic windows on the developing brain are significant. The aim of this paper is to provide a review of the current research into MR imaging of the living fetal brain with the aim of motivating improved interfaces between the two fields. The review begins with a description of faster MRI techniques that are capable of freezing motion of the fetal head during the acquisition of a slice, and how these have been combined with advanced post-processing algorithms to build 3D images from motion scattered slices. Such rich data has motivated the development of techniques to automatically label developing tissue zones within MRI data allowing their quantification in 3D and 4D within the normally growing fetal brain. These methods have provided the basis for later work that has created the first maps of tissue growth rate and cortical folding in normally developing brains in-utero. These measurements provide valuable findings that compliment those derived from post-mortem anatomy, and additionally allow for the possibility of larger population studies of the influence of maternal environmental and genes on early brain development.

Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing gray and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57-22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.

Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation

In recent years, postprocessing of fast multislice magnetic resonance imaging (MRI) to correct fetal motion has provided the first true 3-D MR images of the developing human brain in utero. Early approaches have used reconstruction based algorithms, employing a two-step iterative process, where slices from the acquired data are realigned to an approximate 3-D reconstruction of the fetal brain, which is then refined further using the improved slice alignment. This two step slice-to-volume process, although powerful, is computationally expensive in needing a 3-D reconstruction, and is limited in its ability to recover subvoxel alignment. Here, we describe an alternative approach which we term slice intersection motion correction (SIMC), that seeks to directly co-align multiple slice stacks by considering the matching structure along all intersecting slice pairs in all orthogonally planned slices that are acquired in clinical imaging studies. A collective update scheme for all slices is then derived, to simultaneously drive slices into a consistent match along their lines of intersection. We then describe a 3-D reconstruction algorithm that, using the final motion corrected slice locations, suppresses through-plane partial volume effects to provide a single high isotropic resolution 3-D image. The method is tested on simulated data with known motions and is applied to retrospectively reconstruct 3-D images from a range of clinically acquired imaging studies. The quantitative evaluation of the registration accuracy for the simulated data sets demonstrated a significant improvement over previous approaches. An initial application of the technique to studying clinical pathology is included, where the proposed method recovered up to 15 mm of translation and 30 degrees of rotation for individual slices, and produced full 3-D reconstructions containing clinically useful additional information not visible in the original 2-D slices.

MR imaging of the fetal brain

Fetal MRI is clinically performed to evaluate the brain in cases where an abnormality is detected by prenatal sonography. These most commonly include ventriculomegaly, abnormalities of the corpus callosum, and abnormalities of the posterior fossa. Fetal MRI is also increasingly performed to evaluate fetuses who have normal brain findings on prenatal sonogram but who are at increased risk for neurodevelopmental abnormalities, such as complicated monochorionic twin pregnancies. This paper will briefly discuss the common clinical conditions imaged by fetal MRI as well as recent advances in fetal MRI research.