Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach.

Lung ultrasonography provides relevant information on morphological and functional changes occurring in the lungs. However, it correlates weakly with pulmonary congestion and extra vascular lung water. Moreover, there is lack of consensus on scoring systems and acquisition protocols. The automation of this technique may provide promising easy-to-use clinical tools to reduce inter- and intra-observer variability and to standardize scores, allowing faster data collection without increased costs and patients risks.

Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema.

Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). A digital analysis of pleural line and subpleural space, based on the GLCM with second order statistical texture analysis, was tested. We prospectively evaluated 47 subjects: 16 with a clinical diagnosis of CPE, 8 of ARDS, and 23 healthy subjects. By comparing ARDS and CPE patients' subgroups with HCG, the one-way ANOVA models found a statistical significance in 9 out of 11 GLCM textural features. Post-hoc pairwise comparisons found statistical significance within each matrix feature for ARDS vs. CPE and CPE vs. HCG (P ≤ 0.001 for all). For ARDS vs. HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis.

Quantitative lung ultrasonography: a putative new algorithm for automatic detection and quantification of B-lines.

BACKGROUND: This pilot study was designed to develop a fully automatic and quantitative scoring system of B-lines (QLUSS: quantitative lung ultrasound score) involving the pleural line and to compare it with previously described semi-quantitative scores in the measurement of extravascular lung water as determined by standard thermo-dilution. METHODS: This was a prospective observational study of 12 patients admitted in the intensive care unit with acute respiratory distress and each provided with 12 lung ultrasound (LUS) frames. Data collected from each patient consisted in five different scores, four semi-quantitative (nLUSS, cLUSS, qLUSS, %LUSS) and quantitative scores (QLUSS). The association between LUS scores and extravascular lung water (EVLW) was determined by simple linear regression (SLR) and robust linear regression (RLR) methods. A correlation analysis between the LUS scores was performed by using the Spearman rank test. Inter-observer variability was tested by computing intraclass correlation coefficient (ICC) in two-way models for agreement, basing on scores obtained by different raters blinded to patients' conditions and clinical history. RESULTS: In the SLR, QLUSS showed a stronger association with EVLW (R2 = 0.57) than cLUSS (R2 = 0.45) and nLUSS (R2 = 0.000), while a lower association than qLUSS (R2 = 0.85) and %LUSS (R2 = 0.72) occurred. By applying RLR, QLUSS showed an association for EVLW (R2 = 0.86) comparable to qLUSS (R2 = 0.85) and stronger than %LUSS (R2 = 0.72). QLUSS was significantly correlated with qLUSS (r = 0.772; p = 0.003) and %LUSS (r = 0.757; p = 0.005), but not with cLUSS (r = 0.561; p = 0.058) and nLUSS (r = 0.105; p = 0.744). Moreover, QLUSS showed the highest ICC (0.998; 95%CI from 0.996 to 0.999) among the LUS scores. CONCLUSIONS: This study demonstrates that computer-aided scoring of the pleural line percentage affected by B-lines has the potential to assess EVLW. QLUSS may have a significant impact, once validated with a larger dataset composed by multiple real-time frames. This approach has the potentials to be advantageous in terms of faster data analysis and applicability to large sets of data without increased costs. On the contrary, it is not useful in pleural effusion or consolidations.

High data output method for 3-D correlative light-electron microscopy using ultrathin cryosections.

Investigation of intracellular dynamics requires a detailed description of the molecular topography and ultrastructural morphology of the cell, for example, the position of a protein in relation to a given compartment of the cell and the morphology of the compartment. Standard fluorescence light microscopy (FLM) localizes proteins in living or fixed cells with a resolution of few hundreds of nanometers, but the unlabeled cellular context is partially missing. Electron microscopy (EM) techniques, such as immuno-EM, reveal protein topology with a few tens of nanometer resolution and retain the cellular context. However, EM analysis shows shortcomings compared to FLM, such as, lower statistical output, applicability only to fixed cells, and higher technical difficulties. To bridge the gap between fluorescent cell imaging and EM, several laboratories have developed methods for correlative light-electron microscopy (CLEM). In CLEM, a limited number of fluorescently labeled cell compartments are first imaged by light microscopy and then visualized and analyzed by EM. Recently, two different CLEM approaches using the EM cryo-immunogold method have been developed to extend the analysis to a high number of regions of interest and to correlate the topology of specific antigens. In this chapter, we describe one of these methods, the High Data Output CLEM (HDO-CLEM) approach. The major benefits of HDO-CLEM are the possibility to (1) correlate several hundreds of events at the same time, (2) perform three-dimensional (3D) correlation, (3) immunolabel both endogenous and recombinantly tagged proteins at the same time, and (4) combine the high data analysis capability of FLM with the high precision-accuracy of transmission electron microscopy in a CLEM hybrid morphometric analysis. We have identified and optimized critical steps in sample preparation, defined routines for sample analysis and retracing of regions of interest, developed software for semi/fully automatic 3D FLM reconstruction and defined preliminary conditions for a hybrid light/electron microscopy morphometry approach.

3D HDO-CLEM: cellular compartment analysis by correlative light-electron microscopy on cryosection.

Fundamental to obtaining a depth-understanding of the function and structure of cells is the ability to study and correlate their molecular topography with the ultrastructural morphology, for example, to visualize the position of a given protein relative to a given cell compartment and its morphology. Standard fluorescence light microscopy (FLM) relies on simple sample preparations, and localizes proteins in living or fixed cells with a resolution in the range of few hundred nanometers, allowing large field of view. However, FLM is unable to visualize the unlabeled cellular context. On the other hand, electron microscopy (EM) techniques reveal protein topology with the resolution in a range of a few tens of nanometer, retains the cellular context, but can only be applied on a limited field of view. Therefore, both approaches present shortcomings, in terms of field of view, statistical output, resolution, sample preparation, and context analysis, that can likely complement each other. To bridge the gap between FLM imaging and EM, several laboratories have developed methods for correlative light-electron microscopy (CLEM). In a nutshell, CLEM enables one to investigate the same exact region of interest utilizing the two microscope platforms, and thereby virtually combine their capabilities. In this chapter, we describe a protocol based on immunolabeling of Tokuyasu cryosections that allows correlation of LM and EM images with excellent preservation of cellular ultrastructure. We will refer to this method as high-data-output CLEM (HDO-CLEM). The major benefits of HDO-CLEM are the possibility to (1) correlate several hundreds of events at the same time, (2) perform three-dimensional (3D) correlation, (3) immunolabel both endogenous and recombinantly tagged proteins at the same time, and (4) combine the high data analysis capability of FLM with the high precision of transmission EM in a CLEM hybrid morphometric analysis. We have identified and optimized critical steps in sample preparation, defined routines for sample analysis and retracing of regions of interest, developed software for semi/fully automatic 3D FLM reconstruction and set the basis for a hybrid light/EM morphometry approach.

A novel approach for correlative light electron microscopy analysis.

Correlative light and electron microscopy (CLEM) is a multimodal technique of increasing utilization in functional, biochemical, and molecular biology. CLEM attempts to combine multidimensional information from the complementary fluorescence light microscopy (FLM) and electron microscopy (EM) techniques to bridge the various resolution gaps. Within this approach the very same cell/structure/event observed at level can be analyzed as well by FLM and EM. Unfortunately, these studies turned out to be extremely time consuming and are not suitable for statistical relevant data. Here, we describe a new CLEM method based on a robust specimen preparation protocol, optimized for cryosections (Tokuyasu method) and on an innovative image processing toolbox for a novel type of multimodal analysis. Main advantages obtained using the proposed CLEM method are: (1) hundred times more cells/structures/events that can be correlated in each single microscopy session; (2) three-dimensional correlation between FLM and EM, obtained by means of ribbons of serial cryosections and electron tomography microscopy (ETM); (3) high rate of success for each CLEM experiment, obtained implementing protection of samples from physical damage and from loss of fluorescence; (4) compatibility with the classical immunogold and immunofluorescence labeling techniques. This method has been successfully validated for the correlative analysis of Russel Bodies subcellular compartments.

High data output and automated 3D correlative light-electron microscopy method.

Correlative light/electron microscopy (CLEM) allows the simultaneous observation of a given subcellular structure by fluorescence light microscopy (FLM) and electron microscopy. The use of this approach is becoming increasingly frequent in cell biology. In this study, we report on a new high data output CLEM method based on the use of cryosections. We successfully applied the method to analyze the structure of rough and smooth Russell bodies used as model systems. The major advantages of our method are (i) the possibility to correlate several hundreds of events at the same time, (ii) the possibility to perform three-dimensional (3D) correlation, (iii) the possibility to immunolabel both endogenous and recombinantly expressed proteins at the same time and (iv) the possibility to combine the high data analysis capability of FLM with the high precision-accuracy of transmission electron microscopy in a CLEM hybrid morphometry analysis. We have identified and optimized critical steps in sample preparation, defined routines for sample analysis and retracing of regions of interest, developed software for semi/fully automatic 3D reconstruction and defined preliminary conditions for an hybrid light/electron microscopy morphometry approach.

Three-dimensional microscopy migrates to the web with "PowerUp Your Microscope".

"PowerUp Your Microscope" is a software package designed and realized for the optimization of 3D optical microscopy image quality using the Internet and inverse problems computational approaches. The package is mainly devoted to 3D microscopy users, being operative for wide-field, confocal, and multiphoton microscopy. It provides the microscopy community with an extremely easy and comparatively powerful access to advanced image restoration methods. The core of the computational section is the optical system modeling and inverse deconvolution implementation, which is strongly linked to Web-based software and technology. This project constitutes a real and effective migration to the Web, extending computational approaches to image restoration to the whole microscopy user community, regardless of their background.