Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero.

Quantitative tools are increasingly appealing for decision support in healthcare, driven by the growing capabilities of advanced AI systems. However, understanding the predictive uncertainties surrounding a tool's output is crucial for decision-makers to ensure reliable and transparent decisions. In this paper, we present a case study on pulmonary nodule detection for lung cancer screening, enhancing an advanced detection model with an uncertainty quantification technique called conformal risk control (CRC). We demonstrate that prediction sets with conformal guarantees are attractive measures of predictive uncertainty in the safety-critical healthcare domain, allowing end-users to achieve arbitrary validity by trading off false positives and providing formal statistical guarantees on model performance. Among ground-truth nodules annotated by at least three radiologists, our model achieves a sensitivity that is competitive with that generally achieved by individual radiologists, with a slight increase in false positives. Furthermore, we illustrate the risks of using off-the-shelve prediction models when faced with ontological uncertainty, such as when radiologists disagree on what constitutes the ground truth on pulmonary nodules.

Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks. These networks predict a series of incremental deformation fields that transform the moving image at various spatial frequency levels, ensuring accurate alignment with the fixed image. This multi-resolution approach allows for a more accurate and detailed registration process, capturing both coarse and fine image structures. Our method outperforms existing state-of-the-art techniques, including other multi-resolution strategies, by a substantial margin. Furthermore, we integrate our registration method into a multi-atlas segmentation pipeline and showcase its competitive performance compared to nnU-Net, achieved using only a small subset of annotated images as atlases. This approach is particularly valuable in the context of fetal brain MRI, where annotated datasets are limited. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.

BACKGROUND: Maternal suboptimal nutrition and high stress levels are associated with adverse fetal and infant neurodevelopment.OBJECTIVE: This study aimed to investigate if structured lifestyle interventions involving a Mediterranean diet or mindfulness-based stress reduction during pregnancy are associated with differences in fetal and neonatal brain development.STUDY DESIGN: This was a secondary analysis of the randomized clinical trial Improving Mothers for a Better Prenatal Care Trial Barcelona that was conducted in Barcelona, Spain, from 2017 to 2020. Participants with singleton pregnancies were randomly allocated into 3 groups, namely Mediterranean diet intervention, stress reduction program, or usual care. Participants in the Mediterranean diet group received monthly individual sessions and free provision of extra-virgin olive oil and walnuts. Pregnant women in the stress reduction group underwent an 8-week mindfulness based stress reduction program adapted for pregnancy. Magnetic resonance imaging of 90 fetal brains was performed at 36 to 39 weeks of gestation and the Neonatal Neurobehavioral Assessment Scale was completed for 692 newborns at 1 to 3 months. Fetal outcomes were the total brain volume and lobular or regional volumes obtained from a 3 dimensional reconstruction and semiautomatic segmentation of magnetic resonance images. Neonatal outcomes were the 6 clusters scores of the Neonatal Neurobehavioral Assessment Scale. Multiple regression analyses were conducted to assess the association between the interventions and the fetal and neonatal outcomes.RESULTS: When compared with the usual care group, the offspring exposed to a maternal Mediterranean diet had a larger total fetal brain volume (mean, 284.11 cm(3); standard deviation, 23.92 cm(3) vs 294.01 cm(3); standard deviation, 26.29 cm(3); P=.04), corpus callosum (mean, 1.16 cm(3); standard deviation, 0.19 cm(3) vs 1.26 cm(3); standard deviation, 0.22 cm(3); P=.03), and right frontal lobe (44.20; standard deviation, 4.09 cm3 vs 46.60; standard deviation, 4.69 cm(3); P=.02) volumes based on magnetic resonance imaging measures and higher scores in the Neonatal Neurobehavioral Assessment Scale clusters of autonomic stability (mean, 7.4; standard deviation, 0.9 vs 7.6; standard deviation, 0.7; P=.04), social interaction (mean, 7.5; standard deviation, 1.5 vs 7.8; standard deviation, 1.3; P=.03), and range of state (mean, 4.3; standard deviation, 1.3 vs 4.5; standard deviation, 1.0; P=.04). When compared with the usual care group, offspring from the stress reduction group had larger fetal left anterior cingulate gyri volume (1.63; standard deviation, 0.32 m(3) vs 1.79; standard deviation, 0.30 cm(3); P=.03) based on magnetic resonance imaging and higher scores in the Neonatal Neurobehavioral Assessment Scale for regulation of state (mean, 6.0; standard deviation, 1.8 vs 6.5; standard deviation, 1.5; P<.01).CONCLUSION: Maternal structured lifestyle interventions involving the promotion of a Mediterranean diet or stress reduction during pregnancy were associated with changes in fetal and neonatal brain development.

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.

BACKGROUND AND OBJECTIVEThe automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging.METHODSIn this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness.RESULTSResults show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains.CONCLUSIONSThese findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.