Free-breathing accurate 3D T2 mapping is feasible and may be applicable in myocardial assessment in lieu of current clinical black blood, T2 -weighted techniques.

The positional information theory proposes that a coordinate system provides information to embryonic cells about their position and orientation along a patterning axis. Cells interpret this information to produce the appropriate pattern. During development, morphogens and interpreter transcription factors provide this information. We report a gradient of Meis homeodomain transcription factors along the mouse limb bud proximo-distal (PD) axis antiparallel to and shaped by the inhibitory action of distal fibroblast growth factor (FGF). Elimination of Meis results in premature limb distalization and HoxA expression, proximalization of PD segmental borders, and phocomelia. Our results show that Meis transcription factors interpret FGF signaling to convey positional information along the limb bud PD axis. These findings establish a new model for the generation of PD identities in the vertebrate limb and provide a molecular basis for the interpretation of FGF signal gradients during axial patterning.

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-based Tox_CNN model outperformed other models classifying cells according to health status. Tox_CNN allowed automated extraction of feature maps that clustered compounds according to mechanism of action. Moreover, fully automated region-based CNNs (RCNN) were implemented to detect and classify nuclei, providing per-cell toxicity prediction from raw screening images. We validated both Tox_(R)CNN models for detection of pre-lethal toxicity from nuclei images, which proved to be more sensitive and have broader specificity than established toxicity readouts. These models predicted toxicity of drugs with mechanisms of action other than those they had been trained for and were successfully transferred to other cell assays. The Tox_(R)CNN models thus provide robust, sensitive, and cost-effective tools for in vitro screening of drug-induced toxicity. These models can be adopted for compound prioritization in drug screening campaigns, and could thereby increase the efficiency of drug discovery.

In this work we propose an active surface method to segment complete liver volumes from preoperative CT abdominal images. The method finds the surface that minimizes an energy function combining intensity inside and outside the surface, gradient information and curvature restrictions. The implementation is based on a level set technique following a multi-resolution strategy to reduce computing time. It requires only a single seed point inside the liver to initialize the active surface. The algorithm has been validated on a set of previously diagnosed livers. Resulting segmentations have been supervised by clinicians and radiologists, and numerically evaluated in terms of volume measurements with respect to those obtained from radiologists' manual segmentations. Additionally, radiologists analyzed the necessity of additional corrections on segmenting volumes.