Evaluation of embryo-foetal biometry and its correlation with parturition date in Toy Poodle bitches.

Estimating the parturition date in dogs is challenging due to their reproductive peculiarities that. Ultrasonographic examination serves as a tool for studying embryo/foetal biometry and estimating the time of parturition by measuring foetal and extra-foetal structures. However, due to reproductive differences among various dog breeds, such estimates may have a non-significant pattern, representing inaccuracies in the estimated date of birth. This study aimed to monitor pregnant Toy Poodle bitches and establish relationships between ultrasonographically measured foetal and extra-foetal dimensions and the remaining time until parturition. Eighteen pregnant Toy Poodle bitches were subjected to weekly ultrasonographic evaluations and measurements of the inner chorionic cavity diameter, craniocaudal length (CCL), biparietal diameter (BPD), diameter of the deep portion of diencephalo-telencephalic vesicle (DPTV), abdominal diameter, thorax diameter (TXD), placental thickness and the renal diameter (REND). These parameters were retrospectively correlated with the date of parturition and linear regressions were established between gestational measurements and days before parturition (DBP). All analyses were conducted using the Statistical Package for Social Sciences (IBM® SPSS®) program at a 5% significance level. The foetal measurements that showed a high correlation (r) and reliability (R2) with DBP were BPD [(DBP = [15.538 × BPD] - 39.756), r = .97 and R2 = .93], TXD [(DBP = [8.933 × TXD] - 32.487), r = .94 and R2 = .89], DPTV [(DBP = [34.580 × DPTV] - 39.403), r = .93 and R2 = .86] and REND [(DBP = [13.735 × REND] - 28.937), r = .91 and R2 = .82]. This statistically validates the application of these specific formulas to estimate the parturition date in Toy Poodle bitches.

First-trimester 3D fetal neurosonography: five standardised views.

BACKGROUND: There are several international guidelines for foetal anomalies scanning at 11-14 weeks' gestation. The aim of this study is to present our first-trimester specialist neurosonography protocol with examples of pathology in order to develop a systematic approach to evaluating the first-trimester foetal brain. METHODS: Women undergoing a first-trimester foetal medicine ultrasound scan between 2010 and 2020 for multiple indications underwent neurosonography according to a set protocol. 3D transvaginal brain examination was performed in all cases (2000 pregnancies scanned). We retrospectively reviewed all imaging to develop this protocol. RESULTS: We propose that the following five axial-plane parallel views should be obtained when performing neurosonography in the first trimester, moving from cranial to caudal: 1. Lateral ventricles; 2. Third ventricle; 3. Thalamus and mesencephalon; 4. Cerebellum; 5. Fourth ventricle. Examples of these images and abnormalities that can be seen in each plane are given. CONCLUSIONS: We have presented a specialist protocol for systematically assessing the foetal brain in the first trimester and given examples of pathology which may be seen in each plane. Further work is needed to prospectively assess detection rates of major abnormalities using this protocol and assess the reproducibility and learning curve of this technique.

This article suggests a way in which specialists scanning babies at 11­14 weeks of pregnancy can check the brain in a structured way. This involves looking at the brain at five levels or planes to view the developing structures. The suggested scan protocol is similar to images produced of the brain and heart at the second trimester (20 week) scan. We hope that specialists will find it useful to check the brain in this way if there are concerns raised at the dating (12 week) scan, and that this will lead to earlier detection of brain abnormalities or differences.

Sexual dimorphism of the human fetal pelvis exists at the onset of primary ossification.

Human adolescent and adult skeletons exhibit sexual dimorphism in the pelvis. However, the degree of sexual dimorphism of the human pelvis during prenatal development remains unclear. Here, we performed high-resolution magnetic resonance imaging-assisted pelvimetry on 72 human fetuses (males [M]: females [F], 34:38; 21 sites) with crown-rump lengths (CRL) of 50-225 mm (the onset of primary ossification). We used multiple regression analysis to examine sexual dimorphism with CRL as a covariate. Females exhibit significantly smaller pelvic inlet anteroposterior diameters (least squares mean, [F] 8.4 mm vs. [M] 8.8 mm, P = 0.036), larger subpubic angle ([F] 68.1° vs. [M] 64.0°, P = 0.034), and larger distance between the ischial spines relative to the transverse diameters of the greater pelvis than males. Furthermore, the sacral measurements indicate significant sex-CRL interactions. Our study suggests that sexual dimorphism of the human fetal pelvis is already apparent at the onset of primary ossification.

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks. Designed for optimal computational efficiency, RTSeg-Net minimizes resource demand while significantly enhancing segmentation performance. Our comprehensive evaluation on two distinct intrapartum ultrasound image datasets reveals that RTSeg-Net achieves segmentation accuracy on par with more complex state-of-the-art networks, utilizing merely 1.86 M parameters-just 6 % of their hyperparameters-and operating seven times faster, achieving a remarkable rate of 31.13 frames per second on a Jetson Nano, a device known for its limited computing capacity. These achievements underscore RTSeg-Net's potential to provide accurate, real-time segmentation on low-power devices, broadening the scope for its application across various stages of labor. By facilitating real-time, accurate ultrasound image analysis on portable, low-cost devices, RTSeg-Net promises to revolutionize intrapartum monitoring, making sophisticated diagnostic tools accessible to a wider range of healthcare settings.

Evaluation of twin fetal exposure to radiofrequency field during magnetic resonance imaging.

Fetal development is essential to the human lifespan. As more and more multifetal gestations have been reported recently, clinical diagnosis using magnetic resonance imaging (MRI), which introduced radiofrequency (RF) exposure, raised public concerns. The present study developed two whole-body pregnant models of 31 and 32 gestational weeks (GWs) with twin fetuses and explored RF exposure by 1.5 and 3.0 T MRI. Differences in the relative position of the fetus and changes in fetal weight can cause differences in fetal peak local specific absorption rate averaged over 10 g tissue (pSAR10g). Variation of pSAR10g due to different fetal positions can be ~35%. Numerically, twin and singleton fetal pSAR10g results were not significantly different, however twin results exceeded the limit in some cases (e.g. fetuses of 31 GW at 1.5 T), which indicated the necessity for further research employing anatomically correct twin-fetal models coming from various GWs and particular sequence to be applied.

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

Estimated Fetal Radiation Dose From 18 F-FDG PET/CT During the Second and Third Trimesters of Pregnancy.

PURPOSE: Data published in the literature concerning the doses received by fetuses exposed to a 18 F-FDG PET are reassuring but were obtained from small and heterogeneous cohorts, and very few data are available concerning the fetal dose received after exposure to both PET and CT. The present study aimed to estimate the fetal dose received following a PET/CT exposure using methods that include anthropomorphic phantoms of pregnant women applied on a large cohort. PATIENTS AND METHODS: This retrospective multicenter study included 18 pregnant patients in the second and third trimesters. For PET exposure, the fetal volume and mean concentration of radioactivity in the fetus were measured by manually drawing regions of interest. Those data, combined with the time-integrated activities of the fetus and the mother's organs, were entered into the OLINDA/EXM software 2.0 to assess the fetal dose due to PET exposure. To estimate the fetal dose received due to CT exposure, 2 softwares were used: CT-Expo (based on geometric phantom models of nonpregnant patients) and VirtualDose (using pregnant patient phantoms). RESULTS: The fetal dose exposure for PET/CT examination in the second trimester ranged from 5.7 to 15.8 mGy using CT-Expo (mean, 11.6 mGy) and from 5.1 to 11.6 mGy using VirtualDose (mean, 8.6 mGy). In the third trimester, it ranged from 7.9 to 16.6 mGy using CT-Expo (mean, 10.7 mGy) and from 6.1 to 10.7 mGy using VirtualDose (mean, 7.6 mGy). CONCLUSIONS: The estimated fetal doses were in the same range of those previously published and are well below the threshold for deterministic effects. Pregnancy does not constitute an absolute contraindication for a clinically justified hybrid 18 F-FDG PET/CT.

Investigation on ultrasound images for detection of fetal congenital heart defects.

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

Statistical Characterisation of Fetal Anatomy in Simple Obstetric Ultrasound Video Sweeps.

OBJECTIVE: We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. METHODS: Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) were used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, a total of 760 unique manual annotations from 365 unique pregnancies were used. RESULTS: We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways in which the heatmaps can be applied in computational research (e.g., as a machine learning prior). CONCLUSION: The heatmap parameters are freely available to other researchers (https://github.com/agleed/calopus_statistical_heatmaps).

On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging.

BACKGROUND: To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification. METHODS: We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies. We apply both quantitative and qualitative analyses, using standard metrics (F1-score, sensitivity, and precision), Class Activation Mapping (CAM), and t-Distributed Stochastic Neighbor Embedding (t-SNE). RESULTS: When dealing with high inter-class variability classification tasks, contrastive learning does not bring a significant advantage; whereas it results to be relevant for low inter-class variability classification, specifically when initialized with ImageNet weights. CONCLUSIONS: Contrastive learning approaches are typically used when a large number of unlabeled data is available, which is not representative of US datasets. We proved that SimClr either as pre-training with backbone initialized via ImageNet weights or used in an end-to-end dual-task may impact positively the performance over standard transfer learning approaches, under a scenario in which the dataset is small and characterized by low inter-class variability.

Fetal brain MRI atlases and datasets: A review.

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

Novel hybrid rigid-deformable fetal modeling for simulating the vaginal delivery within the second stage of labor.

BACKGROUND AND OBJECTIVE: The fetal representation as a 3D articulated body plays an essential role to describe a realistic vaginal delivery simulation. However, the current computational solutions have been oversimplified. The objective of the present work was to develop and evaluate a novel hybrid rigid-deformable modeling approach for the fetal body and then simulate its interaction with surrounding fetal soft tissues and with other maternal pelvis soft tissues during the second stage of labor. METHODS: CT scan data was used for 3D fetal skeleton reconstruction. Then, a novel hybrid rigid-deformable model of the fetal body was developed. This model was integrated into a maternal 3D pelvis model to simulate the vaginal delivery. Soft tissue deformation was simulated using our novel HyperMSM formulation. Magnetic resonance imaging during the second stage of labor was used to impose the trajectory of the fetus during the delivery. RESULTS: Our hybrid rigid-deformable fetal model showed a potential capacity for simulating the movements of the fetus along with the deformation of the fetal soft tissues during the vaginal delivery. The deformation energy density observed in the simulation for the fetal head fell within the strain range of 3 % to 5 %, which is in good agreement with the literature data. CONCLUSIONS: This study developed, for the first time, a hybrid rigid-deformation modeling of the fetal body and then performed a vaginal delivery simulation using MRI-driven kinematic data. This opens new avenues for describing more realistic behavior of the fetal body kinematics and deformation during the second stage of labor. As perspectives, the integration of the full skeleton body, especially the upper and lower limbs will be investigated. Then, the completed model will be integrated into our developed next-generation childbirth training simulator for vaginal delivery simulation and associated complication scenarios.

In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences.

PURPOSE: We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS: Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS: The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION: The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.

Ultrasound and magnetic resonance imaging features of fetal urogenital anomalies: A pictorial essay.

This pictorial essay focuses on ultrasound (US) and magnetic resonance imaging (MRI) features of fetal urogenital anomalies. Fetal urogenital malformations account for 30%-50% of all anomalies discovered during pregnancy or at birth. They are usually detected by fetal ultrasound exams. However, when ultrasound data on their characteristics is insufficient, MRI is the best option for detecting other associated anomalies. The prognosis highly depends on their type and whether they are associated with other fetal abnormalities.

Autonomous fetal morphology scan: deep learning + clustering merger - the second pair of eyes behind the doctor.

The main cause of fetal death, of infant morbidity or mortality during childhood years is attributed to congenital anomalies. They can be detected through a fetal morphology scan. An experienced sonographer (with more than 2000 performed scans) has the detection rate of congenital anomalies around 52%. The rates go down in the case of a junior sonographer, that has the detection rate of 32.5%. One viable solution to improve these performances is to use Artificial Intelligence. The first step in a fetal morphology scan is represented by the differentiation process between the view planes of the fetus, followed by a segmentation of the internal organs in each view plane. This study presents an Artificial Intelligence empowered decision support system that can label anatomical organs using a merger between deep learning and clustering techniques, followed by an organ segmentation with YOLO8. Our framework was tested on a fetal morphology image dataset that regards the fetal abdomen. The experimental results show that the system can correctly label the view plane and the corresponding organs on real-time ultrasound movies.Trial registrationThe study is registered under the name "Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning (PARADISE)", project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057. Trial registration: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration 02.11.2023.

Made to move: A review of measurement strategies to characterize heterogeneity in normal fetal movement.

Fetal movement is a crucial indicator of fetal well-being. Characteristics of fetal movement vary across gestation, posing challenges for researchers to determine the most suitable assessment of fetal movement for their study. We summarize the current measurement strategies used to assess fetal movement and conduct a comprehensive review of studies utilizing these methods. We critically evaluate various measurement approaches including subjective maternal perception, ultrasound, Doppler ultrasound, wearable technology, magnetocardiograms, and magnetic resonance imaging, highlighting their strengths and weaknesses. We discuss the challenges of accurately capturing fetal movement, which is influenced by factors such as differences in recording times, gestational ages, sample sizes, environmental conditions, subjective perceptions, and characterization across studies. We also highlight the clinical implications of heterogeneity in fetal movement assessment for monitoring fetal behavior, predicting adverse outcomes, and improving maternal attachment to the fetus. Lastly, we propose potential areas of future research to overcome the current gaps and challenges in measuring and characterizing abnormal fetal movement. Our review contributes to the growing body of literature on fetal movement assessment and provides insights into the methodological considerations and potential applications for research.

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.

This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group. The root mean square error, explained variation, and mean absolute percentage error (MAPE) were utilized for the validity assessment; the two-sample t test, Mann-Whitney U test, Wilcoxon signed-rank test, Bland-Altman plot, and intraclass correlation coefficient (ICC) for the reliability evaluation. Our suggested algorithm outperformed all the competitors in all groups and indices regarding validity, except for the MAPE, where the Inception-v3 surpassed ours in the NPG. The two-sample t test and Mann-Whitney U test indicated no significant difference between the suggested method and the gold standard in group-level comparison. The Wilcoxon signed-rank test revealed significant differences between our new approach and the gold standard in personal-level comparison. All points in Bland-Altman plots fell between the upper and lower limits of agreement. The inter-ICCs of ultrasonographers, our proposed algorithm, and its opponents were graded good reliability, good or moderate reliability, and moderate or poor reliability, respectively. Our proposed approach surpasses the competition and is as reliable as manual measurement.

Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range.

Assessment of fetal radiation exposure in pregnant women undergoing computed tomography and rotational angiography examinations for pelvic trauma.

This study aimed to assess fetal radiation exposure in pregnant women undergoing computed tomography (CT) and rotational angiography (RA) examinations for the diagnosis of pelvic trauma. In addition, this study aimed to compare the dose distributions between the two examinations. Surface and average fetal doses were estimated during CT and RA examinations using a pregnant phantom model and real-time dosemeters. The pregnant model phantom was constructed using an anthropomorphic phantom, and a custom-made abdominal phantom was used to simulate pregnancy. The total average fetal dose received by pregnant women from both CT scans (plain, arterial and equilibrium phases) and a single RA examination was ~60 mGy. Because unnecessary repetition of radiographic examinations, such as CT or conventional 2D angiography can increase the radiation risk, the irradiation range should be limited, if necessary, to reduce overall radiation exposure.