Fetal Cortical Plate Segmentation Using Fully Convolutional Networks With Multiple Plane Aggregation

Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net

Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

Background

Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.

Methods

We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.

Results

Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.

Conclusion

These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.

A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques

BACKGROUND AND PURPOSE

While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques.

MATERIALS AND METHODS

This is a multi-institutional IRB-approved study in which pregnant women with and without opioid use during the current pregnancy were prospectively recruited to undergo fetal MR imaging. A total of 14 opioid-exposed (31.4 ± 2.3 weeks of gestation) and 15 unexposed (31.4 ± 2.4 weeks) fetuses were included. Whole brain volume, cortical plate volume, surface area, sulcal depth, mean curvature, and gyrification index were computed as quantitative features by using our fetal brain MR imaging processing pipeline.

RESULTS

After correcting for gestational age, fetal sex, maternal education, polysubstance use, high blood pressure, and MR imaging acquisition site, all of the global morphologic features were significantly lower in the opioid-exposed fetuses compared with the unexposed fetuses, including brain volume, cortical volume, cortical surface area, sulcal depth, cortical mean curvature, and gyrification index. In regional analysis, the opioid-exposed fetuses showed significantly decreased surface area and sulcal depth in the bilateral Sylvian fissures, central sulci, parieto-occipital fissures, temporal cortices, and frontal cortices.

CONCLUSIONS

In this small cohort, prenatal opioid exposure was associated with altered fetal brain development in the third trimester. This adds to the growing body of literature demonstrating that prenatal opioid exposure affects the developing brain.

Association between placental oxygen transport and fetal brain cortical development: a study in monochorionic diamniotic twins

Normal cortical growth and the resulting folding patterns are crucial for normal brain function. Although cortical development is largely influenced by genetic factors, environmental factors in fetal life can modify the gene expression associated with brain development. As the placenta plays a vital role in shaping the fetal environment, affecting fetal growth through the exchange of oxygen and nutrients, placental oxygen transport might be one of the environmental factors that also affect early human cortical growth. In this study, we aimed to assess the placental oxygen transport during maternal hyperoxia and its impact on fetal brain development using MRI in identical twins to control for genetic and maternal factors. We enrolled 9 pregnant subjects with monochorionic diamniotic twins (30.03 ± 2.39 gestational weeks [mean ± SD]). We observed that the fetuses with slower placental oxygen delivery had reduced volumetric and surface growth of the cerebral cortex. Moreover, when the difference between placenta oxygen delivery increased between the twin pairs, sulcal folding patterns were more divergent. Thus, there is a significant relationship between placental oxygen transport and fetal brain cortical growth and folding in monochorionic twins.

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.

Fully Automated Segmentation of Human Eyeball Using Three-Dimensional U-Net in T2 Magnetic Resonance Imaging

Purpose

To develop and validate a fully automated deep-learning-based tool for segmentation of the human eyeball using a three-dimensional (3D) U-Net, compare its performance to semiautomatic segmentation ground truth and a two-dimensional (2D) U-Net, and analyze age and sex differences in eyeball volume, as well as gaze-dependent volume consistency in normal subjects.

Methods

We retrospectively collected 474 magnetic resonance imaging (MRI) scans, including different gazing scans, from 119 patients. A 10-fold cross-validation was applied to separate the dataset into training, test, and validation sets for both the 3D U-Net and 2D U-Net. Performance accuracy was measured using four quantitative metrics compared to the ground truth, and Bland-Altman plot analysis was conducted. Age and sex differences in eyeball volume and variability in eyeball volume differences across gazing directions were analyzed.

Results

The 3D U-Net outperformed the 2D U-Net with mean accuracy scores >0.95, showing acceptable agreement in the Bland-Altman plot analysis despite a tendency for slight overestimation (mean difference = -0.172 cm³). Significant sex differences and age effects on eyeball volume were observed for both methods (P < 0.05). No significant volume differences were found between the segmentation methods or within each method for the different gazing directions. Significant differences in performance accuracy were identified among the five gazing directions, with the upward direction showing a notably lower performance.

Conclusions

Our study demonstrated the effectiveness of 3D U-Net human eyeball volume segmentation using T2-weighted MRI. The robustness and reliability of 3D U-Net across diverse populations and gaze directions support enhanced ophthalmic diagnosis and treatment strategies.

Translational Relevance

Our findings demonstrate the feasibility of using the proposed 3D U-Net model for the automatic segmentation of the human eyeball, with potential applications in various ophthalmic research fields that require the analysis of 3D geometric eye globe shapes or eye movement detection.

Biometric magnetic resonance imaging analysis of fetal brain development in Down syndrome

OBJECTIVES

To assess brain development in living fetuses with Down syndrome (DS) by biometric measurements on fetal brain magnetic resonance images (MRI).

METHODS

We scanned 10 MRIs of fetuses with confirmed trisomy 21 at birth and 12 control fetal MRIs without any detected anomalies. Fetal brain MRIs were analyzed using 14 fetal brain and skull biometric parameters. We compared measures between DS and controls in both raw MRIs and motion-corrected and anterior-posterior commissure-aligned images.

RESULTS

In the reconstructed images, the measured values of the height of the cerebellar vermis (HV) and anteroposterior diameter of the cerebellar vermis (APDV) were significantly smaller, and the anteroposterior diameter of the fourth ventricle (APDF) was significantly larger in fetuses with DS than controls. In the raw MRIs, the measured values of the right lateral ventricle were significantly larger in fetuses with DS than in controls. Logistic regression analyses revealed that a new parameter, the cerebellar-to-fourth-ventricle ratio (i.e., (APDV * Height of the vermis)/APDF), was significantly smaller in fetuses with DS than controls and was the most predictive to distinguish between fetuses with DS and controls.

CONCLUSIONS

The study revealed that fetuses with DS have smaller cerebellums and larger fourth ventricles compared to the controls.

Review on deep learning fetal brain segmentation from Magnetic Resonance images

Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference

Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (< 1 week) was found in the left central and postcentral sulci and larger variability (>2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification.

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation

Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep learning achieves excellent performance in computer vision, but it is still unsatisfactory for segmenting magnetic resonance images of neonatal brains because they are immature with unique attributes. In this paper, we propose a novel attention-modulated multi-branch convolutional neural network for neonatal brain tissue segmentation. The proposed network is built on the encoder-decoder framework by introducing both multi-scale convolutions in the encoding path and multi-branch attention modules in the decoding path. Multi-scale convolutions with different kernels are used to extract rich semantic features across large receptive fields in the encoding path. Multi-branch attention modules are used to capture abundant contextual information in the decoding path for segmenting brain tissues by fusing both local features and their corresponding global dependencies. Spatial attention connections between the encoding and decoding paths are designed to increase feature propagation for both avoiding information loss during downsampling and accelerating model training convergence. The proposed network was implemented in comparison with baseline methods on three neonatal brain datasets. Our network achieves the average Dice similarity coefficients/the average Hausdorff distances of 0.9116/8.1289, 0.9367/9.8212 and 0.8931/8.1612 on the customized dCBP2021 dataset, 0.8786/11.7863, 0.8965/13.4296 and 0.8539/10.462 on the public NBAtlas dataset, as well as 0.9253/7.7968, 0.9448/9.5472 and 0.9132/7.5877 on the public dHCP2017 dataset in partitioning the brain into gray matter, white matter and cerebrospinal fluid, respectively. The experimental results show that the proposed method achieves competitive state-of-the-art performance in neonatal brain tissue segmentation. The code and pre-trained models are available at https://github.com/zhangyongqin/AMCNN.

Artificial intelligence applied to fetal MRI: A scoping review of current research

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.

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.