3D cell culture has emerged as a promising approach to replicate the complex behaviors of cells within living organisms. This study aims to analyze spatiotemporal behavior of the morphological characteristics of cell structure at multiscale in 3D scaffold-free spheroids using chondrogenic progenitor ATDC5 cells. Over a 14-day culture period, it exhibited cell hypertrophy in the spheroids regarding cellular and nuclear size as well as changes in morphology. Moreover, biological analysis indicated a signification up-regulation of normal chondrocyte as well as hypertrophic chondrocyte markers, suggesting early hypertrophic chondrocyte differentiation. Cell nuclei underwent changes in volume, sphericity, and distribution in spheroid over time, indicating alterations in chromatin organization. The ratio of chromatin condensation volume to cell nuclear volume decreased as the cell nuclei enlarged, potentially signifying changes in chromatin state during hypertrophic chondrocyte differentiation. Our image analysis techniques in this present study enabled detailed morphological measurement of cell structure at multi-scale, which can be applied to various 3D culture models for in-depth investigation.

Structural colors arise from selective light interaction with (nano)structures, which give them advantages over pigmented colors such as resistance to fading and possibility to be fabricated out of traditional low-cost and non-toxic materials. Since the color arises from the photonic (nano)structures, different structural features can impact their photonic response and thus, their color. Therefore, the detailed characterization of their structural features is crucial for further improvement of structural colors. In this work, we present a detailed multi-scale structural characterization of ceramic-based photonic glasses by using a combination of high-resolution ptychographic X-ray computed tomography and small angle X-ray scattering. Our results uncover the structure-processing-properties\' relationships of such nanoparticles-based photonic glasses and point out to the need of a review of the structural features used in simulation models concomitantly with the need for further investigations by experimentalists, where we point out exactly which structural features need to be improved.

The CAS3D image processing method intuitively applies a combination of Fourier space and real space 3D analysis algorithms to volumetric images of single skeletal muscle fiber Myosin II Second Harmonic Generation (SHG) XYZ image data. Our developed tool automatically quantifies the myofibrillar orientation in muscle samples by determining the cosine angle sum of intensity gradients in 3D (CAS3D) while determining the mean sarcomere length (SL) and sample orientation. The expected CAS3D values could be reproduced from ideal artificial data sets. Applied random noise in artificial images lowers the detected CAS3D value, and for noise levels below 20%, the correlation can be approximated by a linear function with a slope of -0.006 CAS3D/noise%. The deviations in SL and orientation detection were determined on ideal and noisy artificial data sets and were statistically indistinguishable from 0 (null hypothesis t-test P > 0.1). The software was applied to a previously published data set of single skeletal muscle fiber volumetric SHG image data from a rat intensive care unit (ICU) model of ventilator-induced diaphragm dysfunction (VIDD) with treatment regimens involving the small anti-inflammatory molecules BGP-15, vamorolone, or prednisolone. Our method reliably reproduced the results of the previous work and improved the standard deviation of the cosine angle sum detection in all sample groups from a mean of 0.03 to 0.008. This improvement is achieved by applying analysis algorithms to the whole volumetric images in 3D in contrast to the previously common method of slice-wise XY analysis.

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5-54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

To determine the validity and reliability of novel digitalized tools for dental plaque detection and explore the benefits and limitations connected to their use.
Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews. All human clinical studies comparing dental plaque detection using digitalized systems against a standard reference were included.
PubMed and Scopus were screened from 01 January 2013 to 28 September 2023. Bibliographies of primary studies and principal peer-reviewed scientific journals were manually searched.
The initial search identified 576 articles, with a total of 13 included in the review, published between 2015 and 2023. Most of the studies included (77 %) were cross-sectional with three being prospective. Digital devices captured 2D and 3D images via cameras and intra-oral scanners, respectively. The Turesky\'s modified plaque index was the most frequent clinical index. Correlation with clinical examination was moderate to strong, with good to excellent intra- and inter-system agreement.
Within the limitations of this scoping review, image analysis-based plaque detection systems demonstrated good correlations with clinical plaque indices, using both 2D and 3D imaging systems. Whilst digital plaque detection devices offer advantages in terms of procedural standardization and reproducibility, they also have limitations, therefore currently, their application should be underpinned by a comprehensive clinical examination.
Digital plaque detection tools, that provide standardized measurements and store acquired images, facilitate more informed feedback to patients. This objective analysis may enhance clinician confidence in their utility for clinical trials and other applications.

Recent developments in technology and research have offered a wide variety of new techniques for image and data analysis within the medical field. Medical research helps doctors and researchers acquire not only knowledge about health and new diseases, but also techniques of prevention and treatment. In particular, radiomic analysis is mainly used to extract quantitative data from medical images and to build a model strong enough to diagnose focal diseases. However, finding a model capable to fit all patient situations is not an easy task. In this paper frame prediction models and classification models are reported in order to predict the evolution of a given data series and determine whether an anomaly exists or not. This article also shows how to build and make use of a convolutional neural network-based architecture aiming to accomplish prediction task for medical images, not only using common computer tomography scans, but also 3D volumes.

BACKGROUND: There is currently no consensus in the literature whether the aetiology of a Class II subdivision is dental, skeletal or both. The aim of this study was to identify and quantify skeletal and dental asymmetries in Class II subdivision malocclusions.
METHODS: CBCTs from 33 Class II subdivision malocclusion patients were used to construct 3D volumetric label maps. Eighteen landmarks were identified. The original scan and associated 3D volumetric label map were mirrored. Registration of the original and mirrored images relative to the anterior cranial base, maxilla and mandible were performed. Surface models were generated, and 3D differences were quantified. Statistical analysis was performed.
RESULTS: Anterior cranial base registration showed significant differences for fossa vertical difference, fossa roll, mandibular yaw, mandibular lateral displacement and lower midline displacement. Regional registrations showed significant differences for antero-posterior (A-P) mandibular length, maxillary roll, A-P maxillary first molar position, maxillary first molar yaw and maxillary first molar roll. Class II subdivision patients also show an asymmetric mandibular length as well as an asymmetric gonial angle. Moderate correlations were found between the A-P molar relationship and fossa A-P difference, mandibular first molar A-P difference, maxillary first molar A-P difference and maxillary first molar yaw.
CONCLUSIONS: This study suggests that Class II subdivisions can result from both significant skeletal and dental factors. Skeletal factors include a shorter mandible as well as posterior and higher displacement of the fossa on the Class II side, resulting in mandibular yaw. Dental factors include maxillary and mandibular first molar antero-posterior asymmetry.

Filamentous fungi produce a wide range of relevant biotechnological compounds. The close relationship between fungal morphology and productivity has led to a variety of analytical methods to quantify their macromorphology. Nevertheless, only a µ-computed tomography (µ-CT) based method allows a detailed analysis of the 3D micromorphology of fungal pellets. However, the low sample throughput of a laboratory µ-CT limits the tracking of the micromorphological evolution of a statistically representative number of submerged cultivated fungal pellets over time. To meet this challenge, we applied synchrotron radiation-based X-ray microtomography at the Deutsches Elektronen-Synchrotron [German Electron Synchrotron Research Center], resulting in 19,940 3D analyzed individual fungal pellets that were obtained from 26 sampling points during a 48 h Aspergillus niger submerged batch cultivation. For each of the pellets, we were able to determine micromorphological properties such as number and density of spores, tips, branching points, and hyphae. The computed data allowed us to monitor the growth of submerged cultivated fungal pellets in highly resolved 3D for the first time. The generated morphological database from synchrotron measurements can be used to understand, describe, and model the growth of filamentous fungal cultivations.

In tumor therapy anti-angiogenic approaches have the potential to increase the efficacy of a wide variety of subsequently or co-administered agents, possibly by improving or normalizing the defective tumor vasculature. Successful implementation of the concept of vascular normalization under anti-angiogenic therapy, however, mandates a detailed understanding of key characteristics and a respective scoring metric that defines an improved vasculature and thus a successful attempt. Here, we show that beyond commonly used parameters such as vessel patency and maturation, anti-angiogenic approaches largely benefit if the complex vascular network with its vessel interconnections is both qualitatively and quantitatively assessed. To gain such deeper insight the organization of vascular networks, we introduce a multi-parametric evaluation of high-resolution angiographic images based on light-sheet fluorescence microscopy images of tumors. We first could pinpoint key correlations between vessel length, straightness and diameter to describe the regular, functional and organized structure observed under physiological conditions. We found that vascular networks from experimental tumors diverted from those in healthy organs, demonstrating the dysfunctionality of the tumor vasculature not only on the level of the individual vessel but also in terms of inadequate organization into larger structures. These parameters proofed effective in scoring the degree of disorganization in different tumor entities, and more importantly in grading a potential reversal under treatment with therapeutic agents. The presented vascular network analysis will support vascular normalization assessment and future optimization of anti-angiogenic therapy.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.