Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can lead to significant improvements in the accuracy and reproducibility of quantitative assessment of the fetal brain with dMRI.

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 a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.

Adam-Troy Castro's story "Arvies," first published in the August 2010 issue of Lightspeed magazine, imagines a society that believes only fetuses have souls. One consequence of this is that it's normal for people to use advanced technology to never leave the womb. "There are two kinds of people in that story--fetuses and the'arvies,' which they ride around in and have fun and replace regularly," Castro says in Episode 519 of the Geek's Guide to the Galaxy podcast. "[The story] bounces back and forth between the point of view of one of these fetuses and those where you go to the basically mindless woman--by design--whose fate is to carry her around." "Arvies" was a huge hit for Castro, winning the 2011 Million Writers Award for best short story and appearing in books such as Nebula Awards Showcase: 2012 and The Year's Best Science Fiction and Fantasy: 2011.

Now researchers from the Institute of Automation of the Chinese Academy of Sciences (CASIA) have developed an intelligent prediction model to achieve non-invasive screening of Down Syndrome using ultrasound image. This work was published in JAMA Network Open on June 21. For decades, ultrasound images have been widely used for screening fetuses with Down Syndrome due to the method's safety, convenience, and low cost. However, using common ultrasound indicators, detection accuracy is less than 80% in actual ultrasound examinations. Invasive methods such as villus biopsy, amniocentesis, and fetal umbilical venipuncture are also commonly used to detect Down Syndrome.

Technology won’t be a problem for its future application, but legal and ethical concerns might, warns Beijing-based researcher.

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.

During the millions of pregnancies that occur in the United States every year, expectant moms learn oodles about their developing fetuses over months of gestation. But the placenta, a vital and temporary organ that shelters the fetus--delivering life-sustaining nutrients and oxygen, getting rid of toxic by-products and modulating the immune system to protect the pregnancy--largely remains a mystery. A team of Children's National Health System research scientists is beginning to provide insights about the poorly understood placenta. Using three-dimensional (3D) magnetic resonance imaging (MRI), the research team characterized the shape, volume, morphometry and texture of placentas during pregnancy and, using a novel framework, predicted with high accuracy which pregnancies would be complicated by fetal growth restriction (FGR). "When the placenta fails to carry out its essential duties, both the health of the mother and fetus can suffer and, in extreme cases, the fetus can die. Because there are few non-invasive tools that reliably assess the health of the placenta during pregnancy, unfortunately, placental disease may not be discovered until too late--after impaired fetal growth already has occurred," says Catherine Limperopoulos, Ph.D., co-director of research in the Division of Neonatology at Children's National Health System and senior author of the study published online July 22 in Journal of Magnetic Resonance Imaging.

This past week, physicians at Children's Hospital in Philadelphia announced that they'd had remarkable success with keeping lamb fetuses alive outside a womb--in a plastic bag filled with warm amniotic fluid, with the fetus' heart circulating blood through a filter to keep it oxygenated. Astonishing pictures of wee unborn laminated lambs quickly spun up the media science-fiction reference engine. Someday, that might be a human baby floating in a next-gen artificial uterus. Talking heads name-checked Gattaca and Brave New World. You could get a whiff of Blade Runner in there.

It is striking just how little we know about human development, especially given we are now decades into the modern era of biology. How is it possible that we understand exquisitely well how worms, fruit flies, and rodents develop, but our own species' development remains a black box? Paul Knoepfler (@pknoepfler) is a stem cell biologist at UC Davis and writes about science at The Niche. His most recent book is GMO Sapiens: The Life-Changing Science of Designer Babies. One big reason is that for a long time, the politics of doing science on human embryos and fetuses have been radioactive.