Proximo-distal positional information encoded by an Fgf-regulated gradient of homeodomain transcription factors in the vertebrate limb.

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.

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

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.

Pluripotency Surveillance by Myc-Driven Competitive Elimination of Differentiating Cells.

The mammalian epiblast is formed by pluripotent cells able to differentiate into all tissues of the new individual. In their progression to differentiation, epiblast cells and their in vitro counterparts, embryonic stem cells (ESCs), transit from naive pluripotency through a differentiation-primed pluripotent state. During these events, epiblast cells and ESCs are prone to death, driven by competition between Myc-high cells (winners) and Myc-low cells (losers).

ESC-Track: A computer workflow for 4-D segmentation, tracking, lineage tracing and dynamic context analysis of ESCs.

Embryonic stem cells (ESCs) can be established as permanent cell lines, and their potential to differentiate into adult tissues has led to widespread use for studying the mechanisms and dynamics of stem cell differentiation and exploring strategies for tissue repair. Imaging live ESCs during development is now feasible due to advances in optical imaging and engineering of genetically encoded fluorescent reporters; however, a major limitation is the low spatio-temporal resolution of long-term 3-D imaging required for generational and neighboring reconstructions. Here, we present the ESC-Track (ESC-T) workflow, which includes an automated cell and nuclear segmentation and tracking tool for 4-D (3-D + time) confocal image data sets as well as a manual editing tool for visual inspection and error correction.

Fast synthesis and bioconjugation of (68) Ga core-doped extremely small iron oxide nanoparticles for PET/MR imaging.

Combination of complementary imaging techniques, like hybrid PET/MRI, allows protocols to be developed that exploit the best features of both. In order to get the best of these combinations the use of dual probes is highly desirable. On this sense the combination of biocompatible iron oxide nanoparticles and 68Ga isotope is a powerful development for the new generation of hybrid systems and multimodality approaches.

Three-dimensional whole-heart T2 mapping at 3T.

Purpose: Detecting variations in myocardial water content with T2 mapping is superior to conventional T2 -weighted MRI since quantification enables direct observation of complicated pathology. Most commonly used T2 mapping techniques are limited in achievable spatial and/or temporal resolution, both of which reduce accuracy due to partial-volume averaging and misregistration between images. The goal of this study was to validate a novel free breathing T2 mapping sequence that overcomes these limitations.

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd-EOB-DTPA-enhanced MRI.

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI).

Optimal multiresolution 3D level-set method for liver segmentation incorporating local curvature constraints.

Advanced liver surgery requires a precise pre-operative planning, where liver segmentation and remnant liver volume are key elements to avoid post-operative liver failure. In that context, level-set algorithms have achieved better results than others, especially with altered liver parenchyma or in cases with previous surgery. In order to improve functional liver parenchyma volume measurements, in this work we propose two strategies to enhance previous level-set algorithms: an optimal multi-resolution strategy with fine details correction and adaptive curvature, as well as an additional semiautomatic step imposing local curvature constraints.

3D liver segmentation in preoperative CT images using a level-sets active surface method.

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.