RNAi using siRNA is a very powerful tool for functional genomics to identify new drug targets and biological pathways. Although their use in epithelial cells is relatively easy and straightforward, transfection in other cell types is still challenging. The authors report the optimization of transfection conditions for Raw 267.4 macrophage cells. The herein described procedure makes use of automated confocal microscopy, enhanced green fluorescent protein (EGFP)-expressing macrophages, and fluorescently labeled siRNAs to simultaneously quantify both siRNA uptake and silencing efficiency. A comparison of 10 commercial transfectants was performed, leading to the selection of the transfectant giving the highest reproducible knock-down effect without inducing cell toxicity or cell activation. Several buffers used for siRNA/lipid complex assembly were tested, and such a study revealed the crucial importance of this parameter. In addition, a kinetics study led to the determination of the optimal siRNA concentration and the best time window for the assay. In an original approach aimed at simultaneously optimizing both the high-throughput screening process and biological factors, optimal reagent volumes and a process flowchart were defined to ensure robust silencing efficiencies during screening. Such an account should pave the way for future genome-wide RNAi research in macrophages and present an optimization procedure for other "hard-totransfect" cell lines. (Journal of Biomolecular Screening 2009:151-160)

High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application. ' 2008 International Society for Advancement of CytometryKey terms image analysis; biological image processing; automation; cytometry; object detection; segmentation; high-content screening AUTOMATED fluorescent microscopy and high-performance computing have allowed the emergence of high-content screening (HCS) as a useful tool in the early stages of drug discovery (1-4). The multidimensional information (''high content'' in HCS) allows for the tackling of biological models inaccessible to unidimensional high-throughput screening (HTS). HCS can also measure multiple effects in a single experiment; for example, the effect of a drug on bacteria (virus, receptor, etc.) and its toxicity on the host target (1,4). HCS therefore has a potential to become a risk/delay/ cost reducer for the later stages of drug development. The last few years have seen a huge increase in image acquisition capacity. Automated fluorescent microscopes can now record more than 40,000 images a day (90 Gb/day), and do so for weeks at a time. HCS is therefore truly becoming high throughput. This evolution introduces fundamental differences with former HCS approaches: (i) The number of images acquired during a HT-HCS campaign [%half a million (4)] requires a fully automated image analysis. (ii) For image analysis not to become the bottleneck of HT-HCS, the rate of image acquisition (%2 s per image) imposes the rate of image analysis. (iii) The amount of data forbids visual control. Results have meaning in a statistical way and must be weighted against a statistically based acceptance criteria. (iv) The measure of quality for a HT-HCS algorithm is a trade off between speed on the one hand and accuracy and robustness on the other. PREVIOUS WORKNumerous HT-HCS applications require a stage of cell segmentation. It most often serves as a basis for subsequent operations, more diverse and specific to a biological assay. While manual (5,6) or semiautomatic (7) cell segmentation methods may be used for HCS, the need for speed and repeatability forbids them for HT-HCS. Most of the cell segmentation algorithms commonly used in HCS suffer from one or many drawbacks that make them ill adapted to HT-HCS use. They may be slow when compared with the

Abstract:A Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. In previous work we investigated which steps could improve the reconstruction performance for an in-vivo setting. In this work we improved this registration by proposing a stable region detection method as well as extracting matchable features based on a deep-learning approach. Finally, we extracted a measure for the image registration quality and the visibility condition. With experiments we show that the image registration performance is increased and more constant. Using these methods a system can be developed that supports the surgeon during the surgery, by giving feedback and providing a more complete overview of the placenta.

Introduction: Paintings are versatile near-planar objects with material characteristics that vary widely. The fact that paint has a material presence is often overlooked, mostly due to the fact that we encounter many of these artworks through two dimensional reproductions. The capture of paintings in the third dimension is not only interesting for study, restoration and conservation, but it also facilitates making three dimensional reproductions through novel 3-D printing methods. No single imaging method is ideally suited to capture the painting's color and topography and each of them have specific drawbacks. We have therefore designed an efficient hybrid imaging system dedicated to capturing paintings in both color and topography with a high resolution. Results: A hybrid solution between fringe projection and stereo imaging is proposed involving two cameras and a projector. Fringe projection is aided by sparse stereo matching to serve as an image encoder. These encoded images processed by the stereo cameras then help solve the correspondence problem in stereo matching, leading to a dense and accurate topographical map, while simultaneously capturing its color. Through high-end cameras, special lenses and filters we capture a surface area of 170 square centimeter with an in-plane effective resolution of 50 micron and a depth precision of 9 micron. Semi-automated positioning of the system and data stitching consequently allows for the capture of larger surfaces. The capture of the 2 square meter big Jewish Bride by Rembrandt yielded 1 billion 3-D points. Conclusion:The reproductive properties of the imaging system are conform the digitization guidelines for cultural heritage. The data has enabled us to make high resolution 3-D prints of the works by Rembrandt and Van Gogh we have captured, and confirms that the system performs well in capturing both the color and depth information.

No abstract