Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis.

Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes.

Investigating efficient CNN architecture for multiple sclerosis lesion segmentation.

Purpose: The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists' efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks with their own different architecture. Architectural choices are rarely explained. We aimed at presenting the relevance of a U-net-like architecture for our specific task and at building an efficient and simple model. Approach: An experimental study was performed by observing the impact of applying different mutations and deletions to a simple U-net-like architecture. Results: The power of the U-net architecture is explained by the joint benefits of using an encoder-decoder architecture and by linking them with long skip connections. Augmenting the number of convolutional layers and decreasing the number of feature maps allowed us to build an exceptionally light and competitive architecture, the minimally parameterized U-net (MPU-net), with only ∼ 30,000 parameters. Conclusion: The empirical study of the U-net has led to a better understanding of its architecture. It has guided the building of the MPU-net, a model far less parameterized than others (at least by a factor of seven). This neural network achieves a human-level segmentation of multiple sclerosis lesions on fluid-attenuated inversion recovery images only. It shows that this segmentation task does not necessitate overly complicated models to be achieved. This gives the opportunity to build more explainable models that can help such methods to be adopted in a clinical environment.

Confidence-based dynamic optimization model for biomedical image mosaicking.

Biomedical image mosaicking is a trending topic. It consists of computing a single large image from multiple observations and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur, or either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/feature-based methods perform poorly. In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mosaicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well.

Compressive Color Pattern Detection using Partial Orthogonal Circulant Sensing Matrix.

One key issue in compressive sensing is to design a sensing matrix that is random enough to have a good signal reconstruction quality and that also enjoys some desirable properties such that orthogonality or being circulant. The classic method to construct such sensing matrices is to first generate a full orthogonal circulant matrix and then select only a few rows. In this paper, we propose a refined construction of orthogonal circulant sensing matrices that generates a circulant matrix where only a given subset of its rows are orthogonal. That way, the generation method is a lot less constrained leading to better sensing matrices and we still have the desired properties. The proposed partial shift-orthogonal sensing matrix is compared to random and learned sensing matrices in the frame of signal reconstruction. This sensing matrix is pattern-dependent and thus efficient to detect color patterns and edges from the measurements of a color image.

Neovascularization detection in diabetic retinopathy from fluorescein angiograms.

Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering). Detections are done using a supervised classification using specific features. Images and their detected neovascularizations are then spatially matched by an image registration. We compute the expansion speed of the liquid that we call diffusion index. This last one specifies the state of the disease, permits indication of the activity of neovascularizations, and allows a follow-up of patients. The method proposed in this paper has been built to be robust, even with laser impacts, to compute a diffusion index.

Photometric reconstruction of a dynamic textured surface from just one color image acquisition.

Textured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performance of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We thus show that it is possible to have similar results with just one color image.

3-D discrete analytical ridgelet transform.

In this paper, we propose an implementation of the 3-D Ridgelet transform: the 3-D discrete analytical Ridgelet transform (3-D DART). This transform uses the Fourier strategy for the computation of the associated 3-D discrete Radon transform. The innovative step is the definition of a discrete 3-D transform with the discrete analytical geometry theory by the construction of 3-D discrete analytical lines in the Fourier domain. We propose two types of 3-D discrete lines: 3-D discrete radial lines going through the origin defined from their orthogonal projections and 3-D planes covered with 2-D discrete line segments. These discrete analytical lines have a parameter called arithmetical thickness, allowing us to define a 3-D DART adapted to a specific application. Indeed, the 3-D DART representation is not orthogonal, It is associated with a flexible redundancy factor. The 3-D DART has a very simple forward/inverse algorithm that provides an exact reconstruction without any iterative method. In order to illustrate the potentiality of this new discrete transform, we apply the 3-D DART and its extension to the Local-DART (with smooth windowing) to the denoising of 3-D image and color video. These experimental results show that the simple thresholding of the 3-D DART coefficients is efficient.

Propiedades de malta diastásica de sorgo blanco / Propierties of white sorghum diastatic malt

Las enzimas de malta diastásica tienen potencial para hidrolizar almidones pregelatinizados liberando azúcar solubles, disminuyendo la viscosidad de mezclas y permitiendo el uso de altas concentraciones de nutrimentos para la preparación de cremas, alimento para infantes y bebidas formuladas con base cereal. Así, es fundamental determinar la capacidad de la malta de sorgo para desarrollar propiedades funcionales deseables como viscosidad, solubilidad en agua y calidad nutrimental. En este trabajo se monitorearon las características de un sorgo blanco durante la germinación y la malta resultante. Se hizo germinar sorgo blanco "Dorado" a 28ºC y 95 por ciento HR durante 6 días en completa oscuridad y después se secó a 55ºC y se pulvorizó para obtener harina de malta diastástica. Se determinaron las características fisoquímicas, químicas, nutrimentales y diastásicas de la malta así como su capacidad para licuar mezclas de harinas precocidas rehidratadas. La máxima actividad diastásica ocurrió a los 3-4 días de germinación. La pérdida de materia seca del grano ocurrida durante la germinación aumentó a razón de 2.7 unidades porcentuales por día. Los períodos de germinación prolongados resultaron en mayores pérdidas de materia seca y menores niveles de actividad diastásica. El índice de solubilidad en agua del sorgo aumentó linealmente durante los primeros 5 días de germinación debido probablemente a la producción de azúcares solubles y aminoácidos libres. Los valores de digestivilidad in vitro de proteina (78.7 g/100 g proteína), el contenido de lisina (3g/100 g de proteína) y el C-PER (1.0) del sorgo aumentaron durante la germinación. La malta de sorgo fue capaz de licuar mezclas precocidas y rehidratadas (20 por ciento) de sólidos) en 5 min. de mezclado a 30ºC. La malta con máxima actividad diastásica es útil para licuar pastas o bebidas de cereal precocido permitiendo aumentar el contenido de sólidos totales y la densidad de nutrimentos mientras se conservan las propiedades líquidas del producto. la calidad proteica mejorada de la malta de sorgo es favorable para su uso en la formulación de alimentos con alta calidad nutrimentall