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 4,420 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We constructed a mini chamber system that was able to maintain cell culture on a microscope for long periods. It is a modified closed system with medium perfusion and CO2 circulation. The closed CO2 circulation and ample air inside the chamber distinguish it from other closed systems. Using different cell lines, the system was shown to be able to support long-term, time-lapse recording. After 229 hours of time-lapse recording, A2058 cells (a melanoma cell line) became overconfluent but still multiplied. Many CAD cells (a murine neuron-like cell line) still moved their cell bodies and kept their neurite-like processes after 28 days of recording. The entire healing process of a scratch-wounded 124 (a bladder cancer cell line) monolayer can be monitored. Such a modified closed system should find many applications in developmental biology, cell biology, and cancer biology where long-term, time-lapse recording is required or when the health of cells is important.
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PMID:Mini chamber system for long-term maintenance and observation of cultured cells. 1572 33

Finite element (FE) analysis of the effect of implant positioning on the performance of cementless total hip replacements (THRs) requires the generation of multiple meshes to account for positioning variability. This process can be labour intensive and time consuming as CAD operations are needed each time a specific orientation is to be analysed. In the present work, a mesh morphing technique is developed to automate the model generation process. The volume mesh of a baseline femur with the implant in a nominal position is deformed as the prosthesis location is varied. A virtual deformation field, obtained by solving a linear elasticity problem with appropriate boundary conditions, is applied. The effectiveness of the technique is evaluated using two metrics: the percentages of morphed elements exceeding an aspect ratio of 20 and an angle of 165 degrees between the adjacent edges of each tetrahedron. Results show that for 100 different implant positions, the first and second metrics never exceed 3% and 3.5%, respectively. To further validate the proposed technique, FE contact analyses are conducted using three selected morphed models to predict the strain distribution in the bone and the implant micromotion under joint and muscle loading. The entire bone strain distribution is well captured and both percentages of bone volume with strain exceeding 0.7% and bone average strains are accurately computed. The results generated from the morphed mesh models correlate well with those for models generated from scratch, increasing confidence in the methodology. This morphing technique forms an accurate and efficient basis for FE based implant orientation and stability analysis of cementless hip replacements.
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PMID:Mesh morphing for finite element analysis of implant positioning in cementless total hip replacements. 1974 73

Reverse engineering (RE) is a powerful tool for generating a CAD model from the 3D scan data of a physical part that lacks documentation or has changed from the original CAD design of the part. The process of digitizing a part and creating a CAD model from 3D scan data is less time consuming and provides greater accuracy than manually measuring the part and designing the part from scratch in CAD. 3D optical scanning technology is one of the measurement methods which have evolved over the last few years and it is used in a wide range of areas from industrial applications to art and cultural heritage. It is also used extensively in the automotive industry for applications such as part inspections, scanning of tools without CAD definition, scanning the casting for definition of the stock (i.e. the amount of material to be removed from the surface of the castings) model for CAM programs and reverse engineering. In this study two scanning experiments of automotive applications are illustrated. The first one examines the processes from scanning to re-manufacturing the damaged sheet metal cutting die, using a 3D scanning technique and the second study compares the scanned point clouds data to 3D CAD data for inspection purposes. Furthermore, the deviations of the part holes are determined by using different lenses and scanning parameters.
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PMID:Implementation of 3D Optical Scanning Technology for Automotive Applications. 2257 95

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
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PMID:Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. 2688 76