Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Adaptive Compressive Sampling for Mid-infrared Spectroscopic Imaging

Fourier transform infrared (FTIR) spectroscopy enables label-free molecular identification and quantification of biological specimens. The resolution of diffraction limited FTIR imaging is poor due to the long optical wavelengths (2.5{\mu}m to 12.5{\mu}m)used and this is particularly limiting in biomedical imaging. Photothermal imaging overcomes this diffraction limit by using a multimodal pump/probe approach. However, these measurements require approximately 1 s per spectrum, making them impractical for large samples. This paper introduces an adaptive compressive sampling technique to dramatically reduce hyperspectral data acquisition time by utilizing both spectral and spatial sparsity. This method identifies the most informative spatial and spectral features and integrates a fast tensor completion algorithm to reconstruct megapixel-scale images and demonstrates speed advantages over FTIR imagin

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming, subjective, and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This technique, called optical photothermal infrared (O-PTIR) imaging, provides a 10X enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present statistically robust validation from 74 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques from up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelium and stroma that exhibits efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology

Crystal plasticity as complementary modelling technique for improved simulations results of anisotropic sheet metal behaviour in forming processes

The accuracy of the simulation results in terms of metal sheet forming strongly depends on the capability of modelling the anisotropic material behaviour. In addition, predictive capabilities of the models are strongly influenced by the way how the constitutive model parameters are calibrated. Macroscopic models lean towards to become more complex in order to map the material behaviour more precisely. As consequence the amount and complexity of the experiments is increasing as well. In addition, it is well known that, some of the experiments, for example the equibiaxial compression test, are difficult to perform and therefore, a reasonable coupling of crystal plasticity (CP) modelling and macroscopic models is proposed. It is worth to mention that, in the domain of CP, arbitrary load cases are possible and therefore, any stress ratio of the yield criterion can be used for calibration. Prediction of anisotropic material behaviour of AA6016-T4 and DC05 sheets based on CP simulations were previously presented and compared with the macroscopic Yld2000-2d model. Their data set is now used for the calibration of the parameters of the macroscopic model, where in contrast to the classical procedure, the exponent of the yield locus is defined as a fitting parameter. The strain distributions predicted by the models have been compared with DIC-measurements of Nakajima samples. The predictive capabilities of the CP-based fitting procedure, compared to the classical fitting, are highlighted. Additionally, a comparison of the strain distribution prediction between all model variants is performed on a cruciform shaped deep drawing part. It underlines the importance of the correct prediction of the yield normal, as it is given by the crystal plasticity computation.ISSN:1757-8981ISSN:1757-899

On the prediction of yield loci based on crystal plasticity models and the spectral solver framework

Prediction of the yield loci based on crystal plasticity material models in combination with an efficient solver, the FFT-based spectral solver, is the main focus of this study. Results of the CP-based yield locus modeling are compared with the well-established macroscopic model YLD2000-2d for various materials; steel as well as aluminum alloys. For this purpose, uniaxial tensile tests in various directions as well as biaxial tests were performed. Further, the influence of grain size in crystal plasticity simulations is often neglected due to the fact that most grains are assumed to have similar size or the influence of grain size is directly mapped within material parameters. For materials containing significantly different grain sizes, this approach does not apply and therefore, a suitable model for crystal plasticity laws is needed. In the framework of this research, an adapted Hall-Petch phenomenological model is implemented in the crystal plasticity open-source code DAMASK. The spectral solver in combination with the phenomenological constitutive laws allows computing of numerical results in short time, which is a key factor for the development of new materials and industrial research.ISSN:1742-6588ISSN:1742-659

Optimized prediction of strain distribution with crystal plasticity supported definition of yielding direction

The predictability of strain distributions and the related prediction of hardening and failure plays a central role in tool and process design for any metal forming process. Studying yielding behaviour it was discovered, that for various physically motivated yield loci [2,5], no satisfying agreement between DIC measured strain distribution and simulation result could be obtained, even after optimization of parameters and for both associated and non-associated flow assumption (e.g. 8 or 16 parameters). In parallel, crystal plasticity simulations were investigated with the objective to predict the relation between stress and strain ratios for a large number of load cases based on texture measurement. The resulting relations were then applied as input parameters for the plastic yield description and without further optimization almost perfect agreement between forming experiment and simulation was reached. The output can be obtained with either free shape yield loci [13,19], or non-associated flow description [15]. This publication thus presents a novel approach to use micro scale predictions of plastic yielding behaviour to calibrate macroscopic models for metals on the example of an AA6016-T4 aluminium alloy