Severe hypertension in pregnancy.

Severe hypertension in pregnancy is defined as a sustained systolic blood pressure of 160 mmHg or over or diastolic blood pressure of 110 mmHg or over and should be assessed in hospital. Severe hypertension before 20 weeks' gestation is rare and usually due to chronic hypertension; assessment for target organ damage and exclusion of secondary hypertension are warranted. The most common cause of severe hypertension in pregnancy is pre-eclampsia, which presents after 20 weeks' gestation.

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI.

In this paper, we present a novel method for the correction of motion artifacts that are present in fetal magnetic resonance imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patchwise optimization, super-resolution, and automatic outlier rejection.

Placenta Maps: In Utero Placental Health Assessment of the Human Fetus.

The human placenta is essential for the supply of the fetus. To monitor the fetal development, imaging data is acquired using (US). Although it is currently the gold-standard in fetal imaging, it might not capture certain abnormalities of the placenta.

Normative biometry of the fetal brain using magnetic resonance imaging.

The fetal brain shows accelerated growth in the latter half of gestation, and these changes can be captured by 2D and 3D biometry measurements. The aim of this study was to quantify brain growth in normal fetuses using Magnetic Resonance Imaging (MRI) and to produce reference biometry data and a freely available centile calculator ( https://www.developingbrain.

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations.