The placenta has roles in fetal growth and development, oxygenation and nutrition, synthesising vital substances for pregnancy maintenance, including estrogen, progesterone, cytokines, and growth factors, and acting as a barrier against pathogens and drugs. Placental dysfunction is a leading cause of perinatal morbidity and mortality, including fetal growth restriction (FGR), pre-eclampsia, and stillbirth [1]. The in vivo assessment of placenta across gestation is critical to understand placental structure, function, and development and to identify strategies to optimise pregnancy outcome [2]. The primary modality for placental evaluation is two-dimensional (2D) ultrasound (US), which is non-invasive, inexpensive and more easily acceptable and accessible than other imaging modalities such as X-ray or Magnetic Resonance Imaging (MRI). It may be used to characterize location, shape, and volume of the placenta along with its interface with the endometrium and myometrium. Three-dimensional Power Doppler (PD) ultrasound permits direct visualisation of multi-directional placental vascularity, allowing assessment of both the uteroplacental and fetoplacental circulations, providing dynamic assessment of blood flow for imaging of abnormalities of the placenta. In three-dimensional (3D) ultrasound, a process called semantic segmentation could be used to separate the placenta for qualitative and quantitative analysis. Placenta segmentation is challenging because of its geometry, position, and appearance as the shape and location of placentas vary greatly across subjects [3] and fetal position can lead to shadowing artefacts. Determination of the placental boundary in relation to the uterine tissue is also challenging due to similar appearances [4], irregularity of boundary and changing size and shape with gestation, posing problems for segmentation [5]. Figure 1 shows 3D ultrasound providing placental visualisation in axial, coronal, and sagittal Springer Nature 2021 L

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to regulate blood exchange among twins. The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision challenge, we released the first largescale multicentre TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-centre fetoscopic data, we provide a benchmark for future research in this field.

Midjourney's founder, David Holz, says it's banning these words as a stopgap measure to prevent people from generating shocking or gory content while the company "improves things on the AI side." Holz says moderators watch how words are being used and what kinds of images are being generated, and adjust the bans periodically. The firm has a community guidelines page that lists the type of content it blocks in this way, including sexual imagery, gore, and even the emoji, which is often used as a symbol for the buttocks. AI models such as Midjourney, DALL-E 2, and Stable Diffusion are trained on billions of images that have been scraped from the internet. Research by a team at the University of Washington has found that such models learn biases that sexually objectify women, which are then reflected in the images they produce.

In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified artificial dataset of images is generated that can be used for training deep learning segmentation models. We have observed that on average the presented method of data augmentation led to a 42% decrease in the binary cross-entropy loss of the validation dataset compared to the common approach in the literature. Additionally, the morphology of the intervillous space is studied under the effect of the proposed image reconstruction technique, and the diversity of the artificially generated population is quantified. Due to the high resemblance of the generated images to the real ones, the applications of the proposed method may not be limited to placental histological images, and it is recommended that other types of tissues be investigated in future studies.

Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Measuring signal changes over time requires segmenting the placenta in each volume of the time series. Due to the large number of volumes in the BOLD time series, existing studies rely on registration to map all volumes to a manually segmented template. As the placenta can undergo large deformation due to fetal motion, maternal motion, and contractions, this approach often results in a large number of discarded volumes, where the registration approach fails. In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series. We use a boundary-weighted loss function to accurately capture the placental shape. Our model is trained and tested on a cohort of 91 subjects containing healthy fetuses, fetuses with fetal growth restriction, and mothers with high BMI. We achieve a Dice score of 0.83 0.04 when matching with ground truth labels and our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series.

Although the placenta can be visualised using ultrasound, measuring it, and all the tiny blood vessels supplying it, is extremely time consuming, making this impractical for routine early pregnancy screening. So the University of Oxford has used machine learning to develop a tool, trained on thousands of ultrasound images where the placenta has been painstakingly marked out by hand, to automate the recognition process.

After a baby is born, doctors sometimes examine the placenta--the organ that links the mother to the baby--for features that indicate health risks in any future pregnancies. Unfortunately, this is a time-consuming process that must be done by a specialist, so most placentas go unexamined after the birth. A team of researchers from Carnegie Mellon University (CMU) and the University of Pittsburgh Medical Center (UPMC) developed a machine learning approach to examine placenta slides so more women can be informed of their health risks. One reason placentas are examined is to look for a type of blood vessel lesions called decidual vasculopathy (DV). These indicate the mother is at risk for pre-eclampsia--a complication that can be fatal to the mother and the baby--in any future pregnancies.

Artificial Intelligence has been one of the greatest technologies developed by humans. The quest for imitating human intelligence might not be a hundred percent accurate. But, it is pretty much helpful in shifting the existing burden of humans to an extent and developing devices that can work independently without the intervention of human beings. Ever since its inception, artificial intelligence had come a long way to become what it is today. It has expanded its domain and giving subfields such as machine learning a great chance to thrive in the world.

Placentas can provide critical information about the health of the mother and baby, but only 20 percent of placentas are assessed by pathology exams after delivery in the U.S. The cost, time and expertise required to analyze them are prohibitive. Now, a team of researchers has developed a novel solution that could produce accurate, automated and near-immediate placental diagnostic reports through computerized photographic image analysis. Their research could allow all placentas to be examined, reduce the number of normal placentas sent for full pathological examination and create a less resource-intensive path to analysis for research -- all of which may positively benefit health outcomes for mothers and babies. "The placenta drives everything to do with the pregnancy for the mom and baby, but we're missing placental data on 95 percent of births globally," said Alison Gernand, assistant professor of nutritional sciences in Penn State's College of Health and Human Development. "Creating a more efficient process that requires fewer resources will allow us to gather more comprehensive data to examine how placentas are linked to maternal and fetal health outcomes, and it will help us to examine placentas without special equipment and in minutes rather than days."

Placentas can provide critical information about the health of the mother and baby, but only 20 percent of placentas are assessed by pathology exams after delivery in the U.S. The cost, time and expertise required to analyze them are prohibitive. Now, a team of researchers has developed a novel solution that could produce accurate, automated and near-immediate placental diagnostic reports through computerized photographic image analysis. Their research could allow all placentas to be examined, reduce the number of normal placentas sent for full pathological examination and create a less resource-intensive path to analysis for research--all of which may positively benefit health outcomes for mothers and babies. "The placenta drives everything to do with the pregnancy for the mom and baby, but we're missing placental data on 95 percent of births globally," said Alison Gernand, assistant professor of nutritional sciences in Penn State's College of Health and Human Development. "Creating a more efficient process that requires fewer resources will allow us to gather more comprehensive data to examine how placentas are linked to maternal and fetal health outcomes, and it will help us to examine placentas without special equipment and in minutes rather than days."