Extract Quantitative Information Research Articles

Analysis and Performance Evaluation of Entropic Thresholding Image Processing Techniques for Electrical Capacitance Tomography Measurement System

To improve image quality generated from the electrical capacitance tomography measurement system, the use of entropic thresholding techniques is investigated in this article. Based on the analysis of the principle of Electrical Capacitance Tomography (ECT) image reconstruction and entropic thresholding, various algorithms have been proposed for easy extraction of quantitative information from tomograms generated from the ECT system. Experiments indicate that proposed algorithms can provide high-quality images at no or minimum computational cost. It is easier to implement and integrate with classical algorithms such as Linear Back Projection, Singular value decomposition, Tikhonov regularization, and Landweber. Entropic thresholding techniques present a feasible and effective way toward the industrial utilization of ECT measurement systems.
 Keywords: Electrical Capacitance Tomography; Inverse Problem; Image Reconstruction; Entropic Thresholding

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

Practical considerations for shallow submerged archaeological prospection with 3‐D electrical resistivity tomography

AbstractDirect current electrical resistivity method has experienced significant breakthroughs during the last decades with the development of advanced instrumentation and sophisticated inversion algorithms. These have substantially benefitted the field of archaeological prospection to extract quantitative information for the buried archaeological material in a complete three‐dimensional (3‐D) context. The obvious continuation of past human occupation and associated settlements in the ultra‐shallow part of coastal zones generated the necessity to compile methodologies for mapping submerged cultural assets. This study investigates the efficiency of dynamic floating and submerged 3‐D Electrical Resistivity Tomography (ERT) for mapping archaeological relics buried beneath the sediments in ultra‐shallow marine environments. Extensive testing is performed through numerical simulation and 3‐D inversion of synthetic apparent resistivity tomographic data. The generic resistivity model including a complex resistive structure embedded in a stratified conductive environmental regime simulated typical scenario of an archaeological feature submerged below the sea water layer. Different array configurations including Dipole–Dipole (DD), Gradient (GRD), Reciprocal Wenner (RecWEN) and Wenner‐Schlumberger (WS), which can be appropriately programmed for continuous off‐shore measurements with associated multichannel resistivity instruments, are validated in order to determine the most efficient one for such surveys. Densely spaced multiple parallel two‐dimensional (2‐D) ERT lines along a single direction composed the survey layout to extract the 3‐D apparent resistivity data set for the different electrode arrays using a 3‐D finite element program. The synthetic tomographic data were corrupted with 3% Gaussian noise and inverted with an iterative smoothness constrained inversion algorithm. The comparative results from the various tested arrays manifest the superiority of the DD in reconstructing the optimum resistivity inversion model for both the floating and submerged survey modes. Additional tests were made concerning the resolving capabilities of ERT with variable seawater thickness and target characteristics. Although accurate ‘a priori’ information regarding the water resistivity and thickness are essential for constraining the 3‐D inversion, erroneous estimation of these parameters can result to misleading results, especially for submerged survey modes. The simulation of floating and submerged 3‐D ERT surveys through synthetic modelling documented the benefits of such approach in reconstructing structured cultural material in shallow off‐shore environments. Finally, the results of a 3‐D dynamic floating ERT survey from a submerged archaeological site in Greece verified the theoretical outcomes, proposing at the same time techniques to overcome problems that can occur due to the unique conditions of the ultra‐shallow marine environment. Overall, this work enhances the conclusion that 3‐D marine ERT is a robust method to reconstruct submerged archaeological structures related to ancient built environment in ultra‐shallow marine regions.

Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis.

Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

Single-molecule localization microscopy.

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.

Soft Modelling of the Photolytic Degradation of Moxifloxacin Combining Surface enhanced Raman Spectroscopy and Multivariate Curve Resolution

The antibiotic moxifloxacin had a recent surge in its use due to its broad spectrum of activity. However, due to the low metabolization inside the organism, it became an environmental concern. Here, the photolytic degradation of moxifloxacin antibiotic in alkaline medium was carried out and monitored through SERS spectroscopy. Multivariate curve resolution method was applied to extract quantitative and kinetic information about the whole process, using correlation constraint to simultaneously quantify the variation of moxifloxacin concentration. The results showed that the photolysis follows an apparent first order kinetics with half-life of 47.5 min. Also, SERS spectrum along with the calculated Raman spectra suggest that cleavage of the diazabicyclonyl substituent is the preferred photodegradation pathway, in agreement with previous reports.

Ultra-Structural Imaging Provides 3D Organization of 46 Chromosomes of a Human Lymphocyte Prophase Nucleus.

Three dimensional (3D) ultra-structural imaging is an important tool for unraveling the organizational structure of individual chromosomes at various stages of the cell cycle. Performing hitherto uninvestigated ultra-structural analysis of the human genome at prophase, we used serial block-face scanning electron microscopy (SBFSEM) to understand chromosomal architectural organization within 3D nuclear space. Acquired images allowed us to segment, reconstruct, and extract quantitative 3D structural information about the prophase nucleus and the preserved, intact individual chromosomes within it. Our data demonstrate that each chromosome can be identified with its homolog and classified into respective cytogenetic groups. Thereby, we present the first 3D karyotype built from the compact axial structure seen on the core of all prophase chromosomes. The chromosomes display parallel-aligned sister chromatids with familiar chromosome morphologies with no crossovers. Furthermore, the spatial positions of all 46 chromosomes revealed a pattern showing a gene density-based correlation and a neighborhood map of individual chromosomes based on their relative spatial positioning. A comprehensive picture of 3D chromosomal organization at the nanometer level in a single human lymphocyte cell is presented.

Algorithmically extracted morphology descriptions for predicting device performance

The device performance of thin film electronics is known to be dependent on morphological properties such as the size, shape, and orientation of aggregates and crystalline domains. So far, descriptions of morphology have been semi-quantitative and loosely linked to device performance. However, by using our recently reported quantitative analysis tool m2py, we create morphology labels and, for the first time, extract quantitative morphology information from scanning probe microscopy measurements. In this work, we use the labels and extracted morphology measurements to validate a novel approach of generating quantitative structure–property relationships through the use of machine learning and regression models; we present this generalizable approach by demonstrating it on organic photovoltaics. In our approach, the open-source toolkit, m2py, is used to label and describe a set of organic photovoltaic devices that have received different thermal annealing treatments, thereby altering their morphologies and, subsequently, their performance. Different regressors and types of morphology descriptions are used to examine the efficacy of quantitative morphology descriptors in predicting device performance. Despite the fact that scanning probe measurements only image the thin film surface, we find that the information-rich nature of morphology data enables accurate device performance predictions, even with datasets that would traditionally be considered too small to generate high-quality predictions. The implications of introducing quantitative morphology information for predictive analysis of other devices and materials is also discussed; given the general and material-agnostic nature of m2py, it is anticipated that the predictive capability of these labels will be ubiquitous to all thin film applications.

An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology

Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation.Methods: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction.Results: Each step of the framework is validated independently both quantitatively and qualitatively by pathologists. An illustration of the different types of features that can be extracted is presented.Conclusion: The proposed generic framework allows for the analysis of the micro-environment surrounding large structures that can be segmented (either manually or automatically). It is independent of the segmentation approach and is therefore applicable to a variety of biomedical research questions.Significance: Chronic tissue remodelling processes after kidney transplantation can result in interstitial fibrosis and tubular atrophy (IFTA) and glomerulosclerosis. This pipeline provides tools to quantitatively analyse, in the same spatial context, information from different consecutive WSIs and help researchers understand the complex underlying mechanisms leading to IFTA and glomerulosclerosis.

Easy-to-Use Osmosis-Based Microfluidic Chip for Protein Crystallization: Application to a Monoclonal Antibody

We developed a disposable microfluidic chip which mimics the vapor diffusion method for exploring protein crystallization conditions but at the nanoliter scale. This device exploits the permeation of water through a thin poly(dimethylsiloxane) (PDMS) layer separating droplets stored in the chip and containing a mixture of proteins and precipitants from an open microfluidic reservoir. The water chemical activity fixed by the reservoir makes it possible to modify the volume of the droplets in a controlled way (reduction or increase). Because PDMS is only permeable to water, the imposed water activity therefore increases or decreases the concentration of the solutes present in the droplet and consequently the supersaturation of the solution. The specificity of our approach, in addition to the low volumes ensuring controlled mass transport conditions, is that the concentration of all the solutes is known at any time, thus allowing the extraction of quantitative information on the crystallization process. We exploit these chips on a protein of therapeutic interest: the full-length monoclonal antibody anti-CD20. Our experiments allow us in particular to estimate both the solubility of this protein and the width of the metastable zone with Na2SO4 and PEG400 as crystallizing agents. We also show that the fine-tuning of the permeation rate makes it possible to perform crystallization/dissolution cycles to selectively dissolve small crystals and increase the mean size of the remaining anti-CD20 crystals.

Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation.

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

Polymer Adsorbents vs. Functionalized Oxides and Carbons: Particulate Morphology and Textural and SurfaceCharacteristics.

Various methods for morphological, textural, and structural characterization of polymeric, carbon, and oxide adsorbents have been developed and well described. However, there are ways to improve the quantitative information extraction from experimental data for describing complex sorbents and polymer fillers. This could be based not only on probe adsorption and electron microscopies (TEM, SEM) but also on small-angle X-ray scattering (SAXS), cryoporometry, relaxometry, thermoporometry, quasi-elastic light scattering, Raman and infrared spectroscopies, and other methods. To effectively extract information on complex materials, it is important to use appropriate methods to treat the data with adequate physicomathematical models that accurately describe the dependences of these data on pressure, concentration, temperature, and other parameters, and effective computational programs. It is shown that maximum accurate characterization of complex materials is possible if several complemented methods are used in parallel, e.g., adsorption and SAXS with self-consistent regularization procedures (giving pore size (PSD), pore wall thickness (PWTD) or chord length (CLD), and particle size (PaSD) distribution functions, the specific surface area of open and closed pores, etc.), TEM/SEM images with quantitative treatments (giving the PaSD, PSD, and PWTD functions), as well as cryo- and thermoporometry, relaxometry, X-ray diffraction, infrared and Raman spectroscopies (giving information on the behavior of the materials under different conditions).

Microplastic pollution monitoring with holographic classification and deep learning

The observation and detection of the microplastic pollutants generated by industrial manufacturing require the use of precise optical systems. Digital holography is well suited for this task because of its non-contact and non-invasive detection features and the ability to generate information-rich holograms. However, traditional digital holography usually requires post-processing steps, which is time-consuming and may not achieve the final object detection performance. In this work, we develop a deep learning-based holographic classification method, which computes directly on the raw holographic data to extract quantitative information of the microplastic pollutants so as to classify them according to the extent of the pollution. We further show that our method can generalize to the classification task of other micro-objects through cross-dataset validation. Without bulky optical devices, our system can be further developed into a portable microplastics detection system, with wide applicability in the monitoring of microplastic particle pollution in the ecological environment.

A combined morphometric approach to feature mouse kidney vasculature

Physiological kidney function is closely related to the state of the vascular network. Disorders, such as capillary rarefaction, predispose to chronic kidney disease (CKD). In this context, deepening of the methodologies for studying the renal vascular network can be of basic importance. To meet this need, numerous animal models and, in parallel, several methods have been developed. In this work we propose a protocol to accurately feature kidney vasculature in mouse, however, the same protocol is suitable to be applied also to other animal models. The approach is multiparametric and mainly based on micro-computed tomography (μCT) technique. Micro-ct allows to study in detail the vascular network of any organ by exploiting the possibility to perfuse the sample with a contrast agent. The proposed protocol provides a fast and reliable method to extract quantitative information from the μCT scan by using only the basic functions of the software supplied by the scanner without any additional analysis. Through iterative cropping of the scanned ROI and calculation of a sample-specific threshold we calculated that the average volume of a female BALB/c kidney of eighth weeks is 147.8 mm3 (5.4%). We also pointed out that the average volume of the vascular network is 4.9% (0.3%). In parallel we performed traditional histological and immunofluorescence techniques to integrate the information gained via μCT and to frame them in the tissue context. Vessel count on histological sections showed a different density in the different regions of the organ parenchyma, in detail, vessel density in the cortex was 19.03 ± 2.51 vessels/ROI while in the medulla it was 10.6 ± 1.7 vessels/ROI and 5.4 ± 1.3 vessels/ROI in the outer and inner medulla, respectively. We then studied vessel distribution in the renal parenchyma which showed that the 55% of vascular component is included in the cortex, the 30% in the outer medulla and the 15% in the inner medulla.Collectively, we propose an integrated approach that can be particularly useful in the preclinical setting to characterize the vasculature of any organ accurately and rapidly.

Automated object tracking, event detection, and recognition for high‐speed video of drop formation phenomena

AbstractOptical imaging technologies have the potential to provide detailed information which can inform process design decisions via modeling of critical phenomena or provide innovative process sensors for use in online monitoring and control strategies. In this work, novel algorithms are developed for automated object tracking, event detection, and classification in high‐speed imaging sequences of drop breakup and coalescence. Using generalization of the physical patterns, a combined strategy extracts quantitative information about the spatiotemporal trajectory and events of every fluid‐object, mapping its lineage and relations to other objects without the need for a training dataset. This high‐level reference frame makes the extracted information easily accessible for evaluation of quantities over sets of objects or events of interest. Included results demonstrate the capability of the software for handling complex multievent scenarios at acceptable execution times, processing 2.7 GB image data (22,000 images/30 videos) in 110–115 s in MATLAB on a standard desktop computer.

Effects of Silicon, Chromium, and Copper on Kinetic Parameters of Precipitation during Tempering of Medium Carbon Steels.

Understanding the tempering behavior of medium carbon steels is mandatory if their mechanical properties are to be improved. For an optimal technology to be developed for this purpose, a substantial experimental basis is needed to extract quantitative information on the microstructure of the tempered material. This paper reports on the characterization of microstructural changes induced by tempering in medium-carbon steels alloyed with Si, Cr, Cu, and Mn using state-of-the-art experimental techniques. Complementarities among these techniques are highlighted. The evolution of transition carbides, cementite, and copper precipitates is described using data from X-ray diffraction, small and ultra-small angle neutron diffraction, transmission electron microscopy, and dilatometry observation. The effects of silicon, chromium, and copper on the mechanism of carbide and copper precipitation are discussed. The considerable changes found in the size and volume of copper precipitates correlate well with the difference in the yield stress between tempered steels with and without copper.

How accurately do mechanophores report on bond scission in soft polymer materials?

AbstractMechanophores are molecules that are incorporated into a host material and react to the local mechanical condition—the state of stress or strain—of that host material. Among their many purposes is that of a reporter: Mechanophores whose optical activity changes in response to mechanical cues can reveal bulk material processes that are ordinarily hidden, such as fatigue and fracture. Moreover, they may do so well before a material is fully fractured. To extract quantitative information from the optical signals from embedded mechanophores it is important that the mechanophores, which are generally a minority component of the material, report proportionally and unambiguously on the mechanical condition of the bulk. This is particularly important for early reporting of damage and wear, for which the optical signal from the mechanophore should accurately reflect bulk bond scission. In this article, we develop and analyze a general theory for the quality of optical mechanoreporting by mechanophores in soft materials, based on the Bell‐Evans theory of bond breaking. We find, that at the typical low fractions in which mechanophores are incorporated the overall change in strength is limited, but that the proportionality of the reporting can be off by significant amounts, particularly at short times after loading but, for non‐scissile bonds, at long times as well.

The ultrafast Kerr effect in anisotropic and dispersive media.

The ultrafast optical Kerr effect (OKE) is widely used to investigate the structural dynamics and interactions of liquids, solutions, and solids by observing their intrinsic nonlinear temporal responses through nearly collinear four-wave mixing. Non-degenerate mixing schemes allow for background free detection and can provide information on the interplay between a material's internal degrees of freedom. Here, we show a source of temporal dynamics in the OKE signal that is not reflective of the internal degrees of freedom but arises from a group index and momentum mismatch. It is observed in two-color experiments on condensed media with sizable spectral dispersion, a common property near an optical resonance. In particular, birefringence in crystalline solids is able to entirely change the character of the OKE signal via the off-diagonal tensor elements of the nonlinear susceptibility. We develop a detailed description of the phase-mismatched ultrafast OKE and show how to extract quantitative information on the spectrally resolved birefringence and group index from time-resolved experiments in one and two dimensions.

Abstract PO-092: Developing a computable phenotype to identify populations eligible/ineligible for lung cancer screening

Abstract Introduction Seventy percent of lung cancer patients are diagnosed at advanced stages. Lung cancer screening (LCS) can potentially produce a stage-shift through early detection of the disease. The 2013 LCS guideline from the U.S. Preventive Services Task Force (USPSTF) recommended screening with low-dose computed tomography (LDCT) for individuals aged between 55 and 80 with 30 pack-year smoking history (i.e., current smoker or had quit smoking within 15 years). However, the high false-positive rate of LCS with LDCT is one of the concerns that hinders the uptake of LCS in real-world settings. An electronic health record (EHR)-based computable phenotyping (CP) algorithm that accurately identifies patients who meet the LCS eligibility criteria can potentially improve the reach of screening eligible population and thereby increase the uptake of LCS. Objective To develop an EHR-based CP algorithm to identify patients eligible for LCS. Method The LCS CP algorithm was developed to extract quantitative smoking information (i.e., pack-years, smoking years, quit year) using both structured EHR and unstructured clinical notes, enabled by advanced natural language processing (NLP) methods. The study cohort consisted of 3,080 patients who received LCS with LDCT based on procedure codes, as documented in EHR data from the UF Health Integrated Data Repository (IDR). The EHR-based LCS CP algorithm included two modules, one to extract smoking information from both structured EHR data and clinical notes using NLP techniques, and the other to integrate the extracted results based on the CP rules (e.g., pack-year > 30; quit year within 15 years; age 55-80) to determine whether a patient is eligible for LCS. For initial evaluation, we conducted a chart review of 20 randomly selected patients and compared the CP algorithm outcomes with the chart review results. Results and Discussion The manual chart review of the 20 patients who underwent LCS with LDCT identified 13 patients were qualified for LCS, 6 patients were not qualified for LCS, and 1 patient was undecidable. Based on this gold standard dataset, the CP algorithm achieved a specificity of 1.00 and a sensitivity of 0.92. Without smoking information extracted from clinical notes using NLP, the specificity score dropped to 0.80. Our results indicate that clinical notes are an important source of information on smoking histories. For all smoking-related information extracted from the clinical notes, smoking history was consistent with the structured EHR in 60% of cases, inconsistent in 10% cases, with the remaining 30% missing. Our results point to (1) suboptimal documentation of smoking information in EHRs, (2) added value of artificial intelligence methods such as NLP in improving CP performance, and (3) potential of an EHR-based CP to accurately identify patients eligible for LCS, and potential relevance to clinical decision support. As the upcoming USPSTF LCS guideline is changing (i.e., from 30 pack-year to 20 pack-year), the CP needs be refined to reflect the changes. Citation Format: Shuang Yang, Tianchen Lyu, Xi Yang, Yonghui Wu, Yi Guo, Michelle Alvarado, Hiren J. Mehta, Ramzi G. Salloum, Dejana Braithwaite, Jinhai Huo, Ya-Chen Tina Shih, Jiang Bian. Developing a computable phenotype to identify populations eligible/ineligible for lung cancer screening [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-092.

Nano-shaping of gold particles on silicon carbide substrate from solid-state to liquid-state dewetting

This work reports on the effect of annealing temperature on the size, shape and wetting of particles obtained on 4H-SiC substrate by the dewetting process of a deposited nanoscale-thick Au film, with focus on the difference between solid-state dewetting (below Au melting temperature) and liquid-state dewetting (above Au melting temperature).After depositing nanoscale-thick Au film on the SiC substrate, annealings are perfomed so to induce the solid-state or liquid-state dewetting process of the film with the consequent formation of particles. Plan-view and cross-view scanning electron microscopy analyses are carried out to quantify the evolution of the average planar size and vertical size of the particles and of the average contact angle of the particles to the SiC surface versus the annealing temperature. These analyses allow us to extract quantitative information on the wetting behaviour of the particles on the SiC surface by calculating the adhesion work versus the annealing temperature. Energy dispersive x-ray analyses are, also, performed on the dewetted particles to analyze their composition in the various annealing conditions. Overall, we set a general framework connecting process parameters to the nano-shape of the dewetted particles towards specific shape design for selected applications.