Straight roads into nowhere – obvious and not-so-obvious biological models for ferrophobic surfaces

• Wilfried Konrad,
• Christoph Neinhuis and
• Anita Roth-Nebelsick

Beilstein J. Nanotechnol. 2022, 13, 1345–1360, doi:10.3762/bjnano.13.111

• segmentation of the water conduit network into compartments. Each pit pore connects two conduits, and this joint pore space is separated by a special nanoporous membrane, the pit membrane (Figure 4). When a conduit becomes dysfunctional due to embolism, the nanopores within the pit membranes prevent the

A new method for obtaining the magnetic shape anisotropy directly from electron tomography images

• Cristian Radu,
• Ioana D. Vlaicu and
• Andrei C. Kuncser

Beilstein J. Nanotechnol. 2022, 13, 590–598, doi:10.3762/bjnano.13.51

• ), segmented data volume (b), different outputs of Magn3t (c, d). XRD pattern of Fe3O4 MNPs and associated Rietveld refinement (in red) (a). Tomviz perspectives on experimental tomogram data as obtained by Genfire (b) and tomogram data after segmentation and particle separation using Magn3t (c). Each of the

Investigation of electron-induced cross-linking of self-assembled monolayers by scanning tunneling microscopy

• Patrick Stohmann,
• Sascha Koch,
• Yang Yang,
• Christopher David Kaiser,
• Julian Ehrens,
• Jürgen Schnack,
• Niklas Biere,
• Dario Anselmetti,
• Armin Gölzhäuser and
• Xianghui Zhang

Beilstein J. Nanotechnol. 2022, 13, 462–471, doi:10.3762/bjnano.13.39

• -processed by using the Gwyddion v.2.41 free software. Analysis of dark spots and subnanometer voids in STM images The dark spots and subnanometer voids in STM images were marked by employing the segmentation function of Gwyddion 2.41. Only the spots inside the phase domains were marked, whereas the domain

The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

• objects of interest, outputting the spatial coordinates defining locations and sizes of bounding boxes, while “localization” is a specific case of detection of a single object in an image [9]. An even finer distinction is “segmentation”. This involves dividing an image into several parts, sets of pixels

• , to locate objects and identify specific structures and boundaries, namely “pixel classification” [7][10]. For example, in medical image diagnosis, the segmentation of organs allows for the quantification of their volume and shape [11]. In many biological applications, nucleus identification

• (segmentation) is used as a good reference for image analysis approaches, that is, cell counting or tracking, and protein localization [12]. The segmentation can be performed as a “semantic segmentation”, which classifies image pixels into specific categories, while “instance segmentation” additionally segments

On the stability of microwave-fabricated SERS substrates – chemical and morphological considerations

• Limin Wang,
• Aisha Adebola Womiloju,
• Christiane Höppener,
• Ulrich S. Schubert and
• Stephanie Hoeppener

Beilstein J. Nanotechnol. 2021, 12, 541–551, doi:10.3762/bjnano.12.44

• acceleration voltage of 5 kV utilizing the in-lens detector. Software and method The automated image processing and analysis to determine the nanoparticle size distributions was performed by using the ImageJ 1.47v software. The workflow involved the application of a particle segmentation algorithm, which uses

The effect of heat treatment on the morphology and mobility of Au nanoparticles

• Sven Oras,
• Sergei Vlassov,
• Simon Vigonski,
• Boris Polyakov,
• Mikk Antsov,
• Vahur Zadin,
• Rünno Lõhmus and
• Karine Mougin

Beilstein J. Nanotechnol. 2020, 11, 61–67, doi:10.3762/bjnano.11.6

• , is annealing time. We believe that the surface effects resulting in the rounding of the NPs are due to energy minimization via a rearrangement of surface atoms, similar to the effect demonstrated recently by Vigonski et al. [17] for heat-induced segmentation of Au nanowires at temperatures much below

Gold-coated plant virus as computed tomography imaging contrast agent

• Alaa A. A. Aljabali,
• Mazhar S. Al Zoubi,
• Khalid M. Al-Batanyeh,
• Ali Al-Radaideh,
• Mohammad A. Obeid,
• Abeer Al Sharabi,
• Walhan Alshaer,
• Bayan AbuFares,
• Tasnim Al-Zanati,
• Murtaza M. Tambuwala,
• Naveed Akbar and
• David J. Evans

Beilstein J. Nanotechnol. 2019, 10, 1983–1993, doi:10.3762/bjnano.10.195

• contrast. This effect is known as paramagnetic relaxation enhancement [2] and is common among contrast agents containing gadolinium [3] and iron oxide nanoparticles [4]. CT is a non-invasive, diagnostic imaging tool that allows for 3-D visual reconstruction and tissue segmentation. It relies on the use of

The electrical conductivity of CNT/graphene composites: a new method for accelerating transmission function calculations

• Olga E. Glukhova and
• Dmitriy S. Shmygin

Beilstein J. Nanotechnol. 2018, 9, 1254–1262, doi:10.3762/bjnano.9.117

• summation over the entire Brillouin zone (BZ) and N is the number of points in the reciprocal space. The accuracy of calculating T(E) is determined by the segmentation of the reciprocal space and the considered number of energies values. The calculation will be reliable when the function T(E) does not

Automated image segmentation-assisted flattening of atomic force microscopy images

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li and
• Huimin Wang

Beilstein J. Nanotechnol. 2018, 9, 975–985, doi:10.3762/bjnano.9.91

• . Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method. Keywords: atomic force microscopy; contour expansion; image flattening; polynomial fitting; sliding window; Introduction Since its invention, the atomic

• case, hundreds of features maybe required to be excluded in one image, which makes the manual operation tedious and time consuming. A robust and automated algorithm for feature detection in MEF is highly awaited. Essentially, the extraction of the exclusion masks is a process of image segmentation

• flattening methods will first be conducted. The sliding-window-based MEF is then introduced, followed by an investigation of the influence of sliding-window size and fitting direction on the flattened images. Finally, the role of image flattening on morphological characterization and image segmentation of

Effect of microtrichia on the interlocking mechanism in the Asian ladybeetle, Harmonia axyridis (Coleoptera: Coccinellidae)

• Jiyu Sun,
• Chao Liu,
• Bharat Bhushan,
• Wei Wu and
• Jin Tong

Beilstein J. Nanotechnol. 2018, 9, 812–823, doi:10.3762/bjnano.9.75

• of the abdomen (Figure 7a–d, viewed from the right side of the H. axyridis). The elytra, hindwings and abdomen are simplified in the different colorized curves. Typically, the abdominal terga show secondary segmentation, with the anterior part of a segment overlapping the posterior part of the

Patterning of supported gold monolayers via chemical lift-off lithography

• Liane S. Slaughter,
• Kevin M. Cheung,
• Sami Kaappa,
• Huan H. Cao,
• Qing Yang,
• Thomas D. Young,
• Andrew C. Serino,
• Sami Malola,
• Jana M. Olson,
• Stephan Link,
• Hannu Häkkinen,
• Anne M. Andrews and
• Paul S. Weiss

Beilstein J. Nanotechnol. 2017, 8, 2648–2661, doi:10.3762/bjnano.8.265

• based on Chan–Vese segmentation [39][40][41]. This algorithm is an enhanced version of a region-based segmentation method that can be used to detect artifacts and differentiates pattern features from topographically uneven backgrounds, which thresholding strategies cannot straightforwardly accomplish

• insulator, a charge neutralizer (flood gun) was used to offset charging of the samples that otherwise impedes spectral acquisition. Doing so, however, causes the peak to shift to lower energies as compared to their expected energy obtained without using a flood gun. Chan–Vese segmentation In our

• and background regions (Figure S8B). During post-segmentation analysis, the histograms of the two regions were plotted and then fit to Gaussian distributions. Because the data were normally distributed, the apparent height of the lifted-off layers was calculated through the difference of the mean of

Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li,
• Shuai Ren and
• Shusheng Bi

Beilstein J. Nanotechnol. 2017, 8, 2572–2582, doi:10.3762/bjnano.8.257

• potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The current segmentation methods suffer from the uneven background in AFM images due to thermal drift and hysteresis of

• AFM scanners. In this study, a two-step approach was proposed to segment NBs and NDs in AFM images in an automated manner. The spherical Hough transform (SHT) and a boundary optimization operation were combined to achieve robust segmentation. The SHT was first used to preliminarily detect NBs and NDs

• . After that, the so-called contour expansion operation was applied to achieve optimized boundaries. The principle and the detailed procedure of the proposed method were presented, followed by the demonstration of the automated segmentation and morphological characterization. The result shows that the

Optical techniques for cervical neoplasia detection

• Tatiana Novikova

Beilstein J. Nanotechnol. 2017, 8, 1844–1862, doi:10.3762/bjnano.8.186

• features related to tissue types were integrated in the conditional random field probabilistic model for the segmentation of images taken after the application of acetic acid. The clinical data from 48 patients were examined with the proposed image analysis algorithm resulting in an average sensitivity of

• (filtering, co-registration) of reflectance and fluorescence images, the reduction of image data by PCA, the image clustering by the K-means clustering algorithm and the use of the nonparametric K-nearest neighbor (KNN) classifier for the image segmentation was implemented by Milbourne et al. [51] for the

Calcium fluoride based multifunctional nanoparticles for multimodal imaging

• Marion Straßer,
• Joachim H. X. Schrauth,
• Sofia Dembski,
• Daniel Haddad,
• Bernd Ahrens,
• Stefan Schweizer,
• Bastian Christ,
• Alevtina Cubukova,
• Marco Metzger,
• Heike Walles,
• Peter M. Jakob and
• Gerhard Sextl

Beilstein J. Nanotechnol. 2017, 8, 1484–1493, doi:10.3762/bjnano.8.148

• IRSnapshotFlash method (TR/TE = 8.7 ms/4.8 ms, matrix: 256 × 176, inplane resolution: 0.7 × 0.7 mm2, slice thickness: 20 mm, number of segments: 44, number of echoes: 128, TA = 4.75 min) [45][46]. Image reconstruction, data fitting and a manually segmentation of the tubes was done offline using Matlab R2012b (The

Zigzag phosphorene nanoribbons: one-dimensional resonant channels in two-dimensional atomic crystals

• Carlos. J. Páez,
• Dario. A. Bahamon,
• Ana L. C. Pereira and
• Peter. A. Schulz

Beilstein J. Nanotechnol. 2016, 7, 1983–1990, doi:10.3762/bjnano.7.189

• tunnelling device [1][13][14], conceived here as an atomically precise segmentation at one of the edges, shows unusual geometry, since the direction of the barriers is perpendicular to the well and contact regions [15][16]. Among our findings we show that for a thin barrier case (constriction with narrow

On the pathway of cellular uptake: new insight into the interaction between the cell membrane and very small nanoparticles

• Claudia Messerschmidt,
• Daniel Hofmann,
• Anja Kroeger,
• Katharina Landfester,
• Volker Mailänder and
• Ingo Lieberwirth

Beilstein J. Nanotechnol. 2016, 7, 1296–1311, doi:10.3762/bjnano.7.121

• inelastic darkfield micrographs were segmented to differentiate Si containing pixels from background applying a semi-automatic image processing procedure. For image segmentation we used the trainable segmentation package (Image J) [48][49] and finally prepared a color coded overlay of the silicon rich areas

• to the TEM brightfield image. Membrane segmentation was done in a similar way, using principal components analysis of spectrum image series [50] prior to segmentation. EELS/EDX For spatially resolved elemental analysis we applied EDX spectroscopy and electron energy loss spectroscopy (EELS). For EDX

Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence

• Yuliang Wang,
• Huimin Wang,
• Shusheng Bi and
• Bin Guo

Beilstein J. Nanotechnol. 2015, 6, 952–963, doi:10.3762/bjnano.6.98

• the assistance of a novel image segmentation method. The method combines the classical threshold method and a modified, active contour method to achieve optimized image segmentation. The image segmentation results obtained with the classical threshold method and the proposed, modified method were

• compared. With the modified method, the diameter, contact angle, and radius of curvature were automatically measured for all NBs in AFM images. The influence of the selection of the threshold value on the segmentation result was discussed. Moreover, the morphological change in the NBs was studied in terms

• of density, covered area, and volume occurring during coalescence under external disturbance. Keywords: atomic force microscopy; characterization; coalescence; nanobubbles; segmentation; Introduction Over the last ten years, spherical-capped bubbles on various hydrophobic surfaces in aqueous

Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

• Torsten Bölke,
• Lisa Krapf,
• Regina Orzekowsky-Schroeder,
• Tobias Vossmeyer,
• Jelena Dimitrijevic,
• Horst Weller,
• Anna Schüth,
• Antje Klinger,
• Gereon Hüttmann and
• Andreas Gebert

Beilstein J. Nanotechnol. 2014, 5, 2016–2025, doi:10.3762/bjnano.5.210

• least in dark image areas, only very few photons are collected per pixel. This unavoidably leads to a low SNR, which not only affects further data interpretation by human observers, but also deteriorates the efficiency of automated processing, segmentation and image analysis [2][3][4]. In our approach

Precise quantification of silica and ceria nanoparticle uptake revealed by 3D fluorescence microscopy

• Adriano A. Torrano and
• Christoph Bräuchle

Beilstein J. Nanotechnol. 2014, 5, 1616–1624, doi:10.3762/bjnano.5.173

• say, Particle_in_Cell-3D substitutes the subjective character of human-assisted image analysis by its unbiased outcomes. The cell segmentation strategy employed by Particle_in_Cell-3D includes the formation of a three-dimensional membrane region. The width of this region is set by the user and defines

• an enlarged transition region between extra- and intracellular spaces. It is much wider than the real cell membrane. The accuracy of the cell segmentation strategy and the typical thickness of the enlarged membrane region were studied by comparing the results achieved with Particle_in_Cell-3D with

• membrane region are also defined in this step. (d) The cell boundaries, or regions of interest, are then used to segment the image of the nanoparticles (yellow outline). The segmentation procedure occurs throughout the image stack, leading to a 3D localization of the particles with respect to the cell. (e

Influence of the PDMS substrate stiffness on the adhesion of Acanthamoeba castellanii

• Sören B. Gutekunst,
• Carsten Grabosch,
• Alexander Kovalev,
• Stanislav N. Gorb and
• Christine Selhuber-Unkel

Beilstein J. Nanotechnol. 2014, 5, 1393–1398, doi:10.3762/bjnano.5.152

• evaluated by manual image segmentation with ImageJ [26]. Statistical significance was analyzed by using a Kruskal–Wallis test and a multiple comparison test in Matlab (MathWorks, USA). Results and Discussion Polydimethylsiloxane (PDMS) has, in recent years, proven to be a versatile tool for cell adhesion

• extensive analysis of the cell areas of A. castellanii by image segmentation of several thousands of acanthamoebae per substrate type. The results of this analysis are summarized in Figure 2 and Figure 3. Figure 2 shows the mean values and standard deviations for the projected cell adhesion areas of A