Multiple Morphological Parameters Research Articles

Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder–decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder–decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

Automatic extraction of multiple morphological parameters of lunar impact craters

AbstractImpact craters are geomorphological features widely distributed on the lunar surface. Their morphological parameters are crucial for studying the reasons for their formation, the thickness of the lunar regolith at the impact site and the age of the impact crater. However, current research on the extraction of multiple morphological parameters from a large number of impact craters within extensive geographical regions faces several challenges, including issues related to coordinate offsets in heterogeneous data, insufficient interpretation of impact crater profile morphology and incomplete extraction of morphological parameters. To address the aforementioned challenges, this paper proposes an automatic extraction method of morphological parameters based on the digital elevation model (DEM) and impact crater database. It involves the correction of heterogeneous data coordinate offset, simulation of impact crater profile morphology and various impact crater morphological parameter automatic extraction. And the method is designed to handle large numbers of impact craters in a wide range of areas. This makes it particularly useful for studies involving regional‐scale impact crater analysis. Experiments were carried out in geological units of different ages and we analysed the accuracy of this method. The analysis results show that: first, the proposed method has a relatively effective impact crater centre position offset correction. Second, the impact crater profile shape fitting result is relatively accurate. The R‐squared value (R2) is distributed from 0.97 to 1, and the mean absolute percentage error (MAPE) is between 0.032% and 0.568%, which reflects high goodness of fit. Finally, the eight morphological parameters automatically extracted using this method, such as depth, depth–diameter ratio, and internal and external slope, are basically consistent with those extracted manually. By comparing the proposed method with a similar approach, the results demonstrate that it is effective and can provide data support for relevant lunar surface research.

Morphometric micro-CT study of contralateral mandibular incisors

ObjectivesThis study aimed to determine the degree of similarity and symmetry in the anatomy of contralateral mandibular incisors. Three-dimensional (3D) models of extracted teeth were obtained from microtomography (micro-CT) scans. Qualitative and quantitative assessments of the morphology and comparison of contralateral pairs were made. The null hypothesis was that contralateral mandibular incisors could not be considered identical in simple morphometric measurements.MethodsSixty pairs of mandibular incisors were extracted from 30 patients and scanned with micro-CT. Virtual models of the cemento-enamel junction to the root apex were rendered. Parameters such as length, canal width, dentinal thicknesses, tortuosity, centerline length, accessory canals, root canal configurations, and root canal orifice cross-sections were used to compare the teeth. Width and thickness comparisons between paired teeth in the same individual were made by paired t-test (Wilcoxon signed-rank test for variables not normally distributed). An online randomization tool generated randomized pairs (independent of the individual/patient). Subsequently, an unpaired t-test (or Mann–Whitney U test for non-normally distributed parameters) and a correlation analysis were conducted. Canal configurations were classified according to preexisting classification schemes. The number and location of accessory canals and apical foramina were registered and compared.ResultsUtilizing advanced imaging techniques and quantitative analyses, our study establishes that contralateral mandibular incisors exhibit a remarkable degree of symmetry in multiple morphological parameters, including length, canal width, and dentinal thicknesses. The apical third showed a high degree of inter-variability for the contralateral pairs. The rigorous statistical analysis of the normalized parameters by Z-score showed no statistically significant differences between the contralateral mandibular incisors. Comparisons between central and lateral teeth revealed differences in root length but no significant disparity in the distribution of accessory canals. Central teeth, on average, were longer, while accessory canals were distributed relatively evenly between central and lateral teeth.ConclusionsThe findings of this study further establish the significant similarities between contralateral mandibular incisors, reinforcing their suitability as a reliable substrate for root canal comparison studies.Clinical relevanceThe absence of statistically significant differences between contralateral pairs in normalized parameters underscores their potential as a reliable reference point for root canal comparison studies in clinical dentistry. Furthermore, our findings emphasize the importance of individualized treatment planning, considering the natural symmetry in mandibular incisors to enhance clinical decision-making. This research contributes valuable insights to the field of endodontics, offering a standardized approach to sample selection and enriching the understanding of dental anatomy.

Quantifying the drug response of patient-derived organoid clusters by aggregated morphological indicators with multi-parameters based on optical coherence tomography.

Patient-derived organoids (PDOs) serve as excellent tools for personalized drug screening to predict clinical outcomes of cancer treatment. However, current methods for efficient quantification of drug response are limited. Herein, we develop a method for label-free, continuous tracking imaging and quantitative analysis of drug efficacy using PDOs. A self-developed optical coherence tomography (OCT) system was used to monitor the morphological changes of PDOs within 6 days of drug administration. OCT image acquisition was performed every 24 h. An analytical method for organoid segmentation and morphological quantification was developed based on a deep learning network (EGO-Net) to simultaneously analyze multiple morphological organoid parameters under the drug's effect. Adenosine triphosphate (ATP) testing was conducted on the last day of drug treatment. Finally, a corresponding aggregated morphological indicator (AMI) was established using principal component analysis (PCA) based on the correlation analysis between OCT morphological quantification and ATP testing. Determining the AMI of organoids allowed quantitative evaluation of the PDOs responses to gradient concentrations and combinations of drugs. Results showed that there was a strong correlation (correlation coefficient >90%) between the results using the AMI of organoids and those from ATP testing, which is the standard test used for bioactivity measurement. Compared with single-time-point morphological parameters, the introduction of time-dependent morphological parameters can reflect drug efficacy with improved accuracy. Additionally, the AMI of organoids was found to improve the efficiency of 5-fluorouracil(5FU) against tumor cells by allowing the determination of the optimum concentration, and the discrepancies in response among different PDOs using the same drug combinations could also be measured. Collectively, the AMI established by OCT system combined with PCA could quantify the multidimensional morphological changes of organoids under the drug's effect, providing a simple and efficient tool for drug screening in PDOs.

Volumetric imaging and morphometric analysis of breast tumor angiogenesis using a new contrast-free ultrasound technique: a feasibility study

BackgroundThere is a strong correlation between the morphological features of new tumor vessels and malignancy. However, angiogenic heterogeneity necessitates 3D microvascular data of tumor microvessels for more reliable quantification. To provide more accurate information regarding vessel morphological features and improve breast lesion characterization, we introduced a quantitative 3D high-definition microvasculature imaging (q3D-HDMI) as a new easily applicable and robust tool to morphologically characterize microvasculature networks in breast tumors using a contrast-free ultrasound-based imaging approach.MethodsIn this prospective study, from January 2020 through December 2021, a newly developed q3D-HDMI technique was evaluated on participants with ultrasound-identified suspicious breast lesions recommended for core needle biopsy. The morphological features of breast tumor microvessels were extracted from the q3D-HDMI. Leave-one-out cross-validation (LOOCV) was applied to test the combined diagnostic performance of multiple morphological parameters of breast tumor microvessels. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of the generated pooled model.ResultsNinety-three participants (mean age 52 ± 17 years, 91 women) with 93 breast lesions were studied. The area under the ROC curve (AUC) generated with q3D-HDMI was 95.8% (95% CI 0.901–1.000), yielding a sensitivity of 91.7% and a specificity of 98.2%, that was significantly higher than the AUC generated with the q2D-HDMI (p = 0.02). When compared to q2D-HDMI, the tumor microvessel morphological parameters obtained from q3D-HDMI provides distinctive information that increases accuracy in differentiating breast tumors.ConclusionsThe proposed quantitative volumetric imaging technique augments conventional breast ultrasound evaluation by increasing specificity in differentiating malignant from benign breast masses.

Feature-based volumetric defect classification in metal additive manufacturing

Volumetric defect types commonly observed in the additively manufactured parts differ in their morphologies ascribed to their formation mechanisms. Using high-resolution X-ray computed tomography, this study analyzes the morphological features of volumetric defects, and their statistical distribution, in laser powder bed fused Ti-6Al-4V. The geometries of three common types of volumetric defects; i.e., lack of fusions, gas-entrapped pores, and keyholes, are quantified by nine parameters including maximum dimension, roundness, sparseness, aspect ratio, and more. It is shown that the three defect types share overlaps of different degrees in the ranges of their morphological parameters; thus, employing only one or two parameters cannot uniquely determine a defect’s type. To overcome this challenge, a defect classification methodology incorporating multiple morphological parameters has been proposed. In this work, by employing the most discriminating parameters, this methodology has been shown effective when implemented into decision tree (>98% accuracy) and artificial neural network (>99% accuracy).

Parametrization of irregularity of urban morphologies for designing better pedestrian wind environment in high-density cities – A wind tunnel study

Breezeways are crucial for horizontal urban ventilation. However, their morphologies, which are highly irregular, still lack investigation. This study aims to gain a categorical understanding of how irregular breezeway morphologies affect pedestrian-level wind performance in high-density cities. Firstly, we extracted breezeway areas and centerlines by adopting different definitions of breezeways: Method 1 defining all near-ground open-spaces (i.e., roads, other non-building areas, and low-rise-building areas) as breezeways; Method 2 defining roads as breezeways. Secondly, multiple morphological parameters were developed to describe irregularity (i.e., fragmentation, angularity, and sinuosity) and permeability of 19 typical urban sites in Hong Kong. Thirdly, linear regression analysis was conducted to correlate the parameters with wind tunnel data. The results show that in a dense built environment, urban ventilation relies on morphologies of all near-ground open-spaces rather than only roads. The regression analysis reveals a negative correlation between pedestrian-level wind performance and urban irregularity, where fragmentation has the largest impact due to the increase of building wall surfaces and viscous friction. The regression analysis reveals a positive correlation between pedestrian-level wind performance and urban permeability, where the newly-defined parameter (i.e., open-space width), which incorporates fragmentation into calculation, achieves higher coefficient of determination than the widely-used parameter (i.e., open-space coverage ratio). The newly-developed regression model, which considers both urban permeability and irregularity, predicts wind speed with a simple mathematical manipulation and a comparable accuracy to models in previous studies. More importantly, it provides applicable spatial information of urban irregularity for design of open-spaces and improvement of urban ventilation.

Beads and Globules from Fires: Can They Be Differentiated through Metallurgical Analysis Based on Machine Learning Algorithms?

Electrical apparatuses are prone to faults, which generally causes fires. During such fires, the identification of resolidified copper beads on wires has a strong influence on the direction of the fire investigation. There are four kinds of resolidified beads formed on copper conductors that have been through the fire with and without voltage, namely, ‘cause’ beads (CB), ‘victim’ beads (VB), overload globules (OG), and fire melting globules (FG). First, to improve the identification’s objectivity and quantifiability, we used various morphologic parameters of crystals and porosities to express metallurgical microcharacteristics, such as Ar-G, As-G, An-G, Dm-G, R-G, FD-G, Fm-G, Ar-G, As-P, An-P, Dm-P, R-P, FD-P, Fm-P, P3-P, and Cu2O. Then, several machine learning classifiers were developed to predict the melted beads based on metallurgical morphologic parameters by using SVM, BP neutral network (BPNN), AdaBoost, bagging, and random forest (RF), respectively. Models were trained and tested based on the sample set, consisting of 560 samples which were collected from real room fires. ACC/F1 of the RF model were 0.894/0.805, respectively, which are superior to SVM, BPNN, AdaBoost, and bagging. For the RF classifier, the recall rates of CB, VB, OG, and FG were 92.5%, 67.5%, 100%, and 97.5%, respectively, indicating that RF has best potential to predict OG and FG. The variable importance was analyzed to distinguish key features, and the results revealed that Cu2O has highest impact on bead classification. We cannot find much promise with this method that uses multiple metallurgical and morphological parameters for distinguishing between CB and VB. It is confirmed that no machine learning classifiers combined with metallurgical analysis could do this work well in this paper. Thus, we strongly recommend that other evidence for investigation in the room fire should also be considered to cover the shortage of this kind.

GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z < 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (<5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as highly bulge dominated) to predictions in regions with high residuals and verify that those labels are ≳97% accurate. To the best of our knowledge, GaMPEN is the first machine-learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.

Screening of salt tolerance traits and the salt tolerance evaluation method in &lt;em&gt;Brassica napus&lt;/em&gt; at the seed germination stage

Soil salinity is one of the major abiotic stresses that negatively affect plant growth and agricultural productivity. For many crop species, the germination stage is one of the most sensitive stages to salinity stress. This study evaluated salt tolerance in 200 Brassica napus L. germplasms using hierarchical cluster analysis based on multiple morphological parameters, including germination rate, root length, fresh weight of root, shoot length, fresh weight of shoot, and total fresh weight. Membership function was used as a comprehensive index to select and evaluate salt tolerance of these germplasms, identifying 8 highly salt-tolerant germplasms, 40 salt-tolerant germplasms, 65 moderate salt-tolerant germplasms, 52 salt-sensitive germplasms, and 35 highly saltsensitive germplasms lines. The responses of rapeseed germplasm to salt stress indicate differences in morphological parameters. Furthermore, NaCl showed a positive effect on total fresh weight and biomass production of some germplasms at a concentration of 100 mmol L–1. Since the correlation value of salt tolerance with total fresh weight was highest under 200 mmol L–1 NaCl, it can be considered the most reliable parameter to evaluate salt tolerance. Therefore, the findings of this study can be applied as an effective and reliable method for mass screening and evaluation of Brassica napus germplasm at the germination stage for breeding salt-tolerant rapeseed genotypes. Highlights - The salinity tolerance of 200 varieties of B. napus germplasms was investigated. - B. napus is more vulnerable to saline conditions during the germination and early reproductive stages than the vegetative and flowering periods. - Based on hierarchical cluster analysis, there was a wide variability of salinity tolerance among rapeseed germplasm. - Low concentration of sodium chloride had a positive effect on shoot and root growth, germination and total weight in some B. napus seedlings. - Total fresh weight can be utilized as the most efficient index for mass screening of salt tolerance in B. napus germplasms at the germination stage.

Somatic Dimorphism in Cercariae of a Bird Schistosome.

Phenotypic polymorphism is a commonly observed phenomenon in nature, but extremely rare in free-living stages of parasites. We describe a unique case of somatic polymorphism in conspecific cercariae of the bird schistosome Trichobilharzia sp. “peregra”, in which two morphs, conspicuously different in their size, were released from a single Radix balthica snail. A detailed morphometric analysis that included multiple morphological parameters taken from 105 live and formalin-fixed cercariae isolated from several naturally infected snails provided reliable evidence for a division of all cercariae into two size groups that contained either large or small individuals. Large morph (total body length of 1368 and 1339 μm for live and formalin-fixed samples, respectively) differed significantly nearly in all morphological characteristics compared to small cercariae (total body length of 976 and 898 μm for live and formalin samples, respectively), regardless of the fixation method. Furthermore, we observed that small individuals represent the normal/commonly occurring phenotype in snail populations. The probable causes and consequences of generating an alternative, much larger phenotype in the parasite infrapopulation are discussed in the context of transmission ecology as possible benefits and disadvantages facilitating or preventing the successful completion of the life cycle.

LeafNet: a tool for segmenting and quantifying stomata and pavement cells.

Stomata play important roles in gas and water exchange in leaves. The morphological features of stomata and pavement cells are highly plastic and are regulated during development. However, it is very laborious and time-consuming to collect accurate quantitative data from the leaf surface by manual phenotyping. Here, we introduce LeafNet, a tool that automatically localizes stomata, segments pavement cells (to prepare them for quantification), and reports multiple morphological parameters for a variety of leaf epidermal images, especially bright-field microscopy images. LeafNet employs a hierarchical strategy to identify stomata using a deep convolutional network and then segments pavement cells on stomata-masked images using a region merging method. LeafNet achieved promising performance on test images for quantifying different phenotypes of individual stomata and pavement cells compared with six currently available tools, including StomataCounter, Cellpose, PlantSeg, and PaCeQuant. LeafNet shows great flexibility, and we improved its ability to analyze bright-field images from a broad range of species as well as confocal images using transfer learning. Large-scale images of leaves can be efficiently processed in batch mode and interactively inspected with a graphic user interface or a web server (https://leafnet.whu.edu.cn/). The functionalities of LeafNet could easily be extended and will enhance the efficiency and productivity of leaf phenotyping for many plant biologists.

Common and Distinct Disruptions of Cortical Surface Morphology Between Autism Spectrum Disorder Children With and Without SHANK3 Deficiency.

SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.

Non-Invasive Ultrasonic Description of Tumor Evolution.

Simple SummaryDuring tumor evolution, heterogeneous structural and functional changes occur in the tumor microenvironment. These complex changes have pro- or anti-tumorigenesis effects and have an impact on therapy efficiency. Therefore, the tumor microenvironment needs to be non-invasively characterized over time. The aim of this preclinical work is to compare the sensitivity of modifications occurring during tumor evolution of volume, immunohistochemistry and non-invasive quantitative ultrasound parameters (Shear Wave Elastography and dynamic Contrast-Enhanced Ultrasound) and to study the link between them. The complementary evaluation over time of multiple morphological and functional parameters during tumor growth underlines the need to integrate histological, morphological, functional, and, ultimately, genomic information into models that can consider the temporal and spatial variability of features to better understand tumor evolution.Purpose: There is a clinical need to better non-invasively characterize the tumor microenvironment in order to reveal evidence of early tumor response to therapy and to better understand therapeutic response. The goals of this work are first to compare the sensitivity to modifications occurring during tumor growth for measurements of tumor volume, immunohistochemistry parameters, and emerging ultrasound parameters (Shear Wave Elastography (SWE) and dynamic Contrast-Enhanced Ultrasound (CEUS)), and secondly, to study the link between the different parameters. Methods: Five different groups of 9 to 10 BALB/c female mice with subcutaneous CT26 tumors were imaged using B-mode morphological imaging, SWE, and CEUS at different dates. Whole-slice immunohistological data stained for the nuclei, T lymphocytes, apoptosis, and vascular endothelium from these tumors were analyzed. Results: Tumor volume and three CEUS parameters (Time to Peak, Wash-In Rate, and Wash-Out Rate) significantly changed over time. The immunohistological parameters, CEUS parameters, and SWE parameters showed intracorrelation. Four immunohistological parameters (the number of T lymphocytes per mm2 and its standard deviation, the percentage area of apoptosis, and the colocalization of apoptosis and vascular endothelium) were correlated with the CEUS parameters (Time to Peak, Wash-In Rate, Wash-Out Rate, and Mean Transit Time). The SWE parameters were not correlated with the CEUS parameters nor with the immunohistological parameters. Conclusions: US imaging can provide additional information on tumoral changes. This could help to better explore the effect of therapies on tumor evolution, by studying the evolution of the parameters over time and by studying their correlations.

Computational analysis of the effects of nitrogen source and sin1 knockout on biosilica morphology in the model diatom Thalassiosira pseudonana

Morphogenesis of the silica based cell walls of diatoms, a large group of microalgae, is a paradigm for the self-assembly of complex 3D nano- and microscale patterned inorganic materials. In recent years, loss-of-function studies using genetic manipulation were successfully applied for the identification of genes that guide silica morphogenesis in diatoms. These studies revealed that the loss of one gene can affect multiple morphological parameters, and the morphological changes can be rather subtle being blurred by natural variations in morphology even within the same clone. Both factors severely hamper the identification of morphological mutants using subjective by-eye inspection of electron micrographs. Here we have developed automated image analysis for objectively quantifying the morphology of ridge networks and pore densities from numerous electron micrographs of diatom biosilica. This study demonstrated differences in ridge network morphology and pore density in diatoms growing on ammonium rather than nitrate as the sole nitrogen source. Furthermore, it revealed shortcomings in previous by-eye evaluation of the biosilica phenotype of the silicanin-1 knockout mutant. We anticipate that the computational methods established in the present work will be invaluable for unraveling genotype–phenotype correlations in diatom biosilica morphogenesis.

Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery

Background and aim Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery. Methods This study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs). Results The intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p < .001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 (p < .001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm2 to 76.31 ± 11.31mm2 (all p < .001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients. Conclusion This technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients’ photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.

Correlation of Multiple Acromion Morphological Parameters on Radiographs in a Geriatric Chinese Population and Its Clinical Significance.

IntroductionPrevious clinical studies have reported associations between the acromion index, lateral acromion angle, and critical shoulder angle and the occurrence of rotator cuff tears. The objective of this study was to analyze the correlations of these different anatomic parameters in geriatric Chinese Population.MethodsHealthy geriatric Chinese participants (n = 66) and geriatric Chinese patients with rotator cuff tears (n = 70) identified between January 2019 and October 2020 were included in this study. Standardized true anteroposterior radiographs were used to measure the acromion index, lateral acromion angle, and critical shoulder angle in each study participant.ResultsThe mean acromion index was significantly larger, the mean lateral acromion angle was significantly smaller, and the mean critical shoulder angle was significantly larger in geriatric patients with full-thickness rotator cuff tears compared with geriatric healthy participants.ConclusionThere were a negative linear relationship between the acromion index and lateral acromion angle and a positive linear relationship between the acromion index and critical shoulder angle in geriatric patients with rotator cuff tear and geriatric healthy participants; we termed this phenomenon “Hypothesis of Acromion Index.” The acromion index, lateral acromion angle, and critical shoulder angle are independent predictors of rotator cuff tears in a geriatric Chinese population.

Predicting quality decay in continuously passaged mesenchymal stem cells by detecting morphological anomalies

With rapid advances in cell therapy, technologies enabling both consistency and efficiency in cell manufacturing are becoming necessary. Morphological monitoring allows practical quality maintenance in cell manufacturing facilities, but relies heavily on human skill. For more reproducible and data-driven quality evaluation, image-based morphological analysis provides multiple advantages over manual observation. Our group has investigated the performance of multiple morphological parameters obtained from time-course images to non-invasively and quantitatively predict cellular quality using machine learning algorithms. Although such morphology-based computational models succeeded in early cell quality predictions, it was difficult to introduce our approach in cell manufacturing facilities owing to data variation issues. Since manufacturing facilities have fixed their protocol to minimize anomalies as much as possible, most accumulated data are normal, and anomalies are scarce. Thus, our morphological analysis had to adapt to such practical situation where it was difficult to observe a wide range of data variations, including both normal samples and anomalies, which is typically essential to improve most machine learning models' performance. In the present study, we introduce a practical morphological analysis concept by investigating the performance of anomalous quality decay discrimination during the continuous passaging of human mesenchymal stem cells (hMSCs). Combining the visualization method and asymmetric statistic discrimination, we describe an effective morphology-based, in-process quality monitoring concept to detect quality anomalies throughout cell culture process. Our results showed that the use of morphological parameters to reflect cellular population heterogeneity can predict hMSC quality decay within 6h after seeding.

A Multiparametric Assay Platform for Simultaneous In Vivo Assessment of Pronephric Morphology, Renal Function and Heart Rate in Larval Zebrafish.

Automated high-throughput workflows allow for chemical toxicity testing and drug discovery in zebrafish disease models. Due to its conserved structural and functional properties, the zebrafish pronephros offers a unique model to study renal development and disease at larger scale. Ideally, scoring of pronephric phenotypes includes morphological and functional assessments within the same larva. However, to efficiently upscale such assays, refinement of existing methods is required. Here, we describe the development of a multiparametric in vivo screening pipeline for parallel assessment of pronephric morphology, kidney function and heart rate within the same larva on a single imaging platform. To this end, we developed a novel 3D-printed orientation tool enabling multiple consistent orientations of larvae in agarose-filled microplates. Dorsal pronephros imaging was followed by assessing renal clearance and heart rates upon fluorescein isothiocyanate (FITC)-inulin microinjection using automated time-lapse imaging of laterally positioned larvae. The pipeline was benchmarked using a set of drugs known to induce developmental nephrotoxicity in humans and zebrafish. Drug-induced reductions in renal clearance and heart rate alterations were detected even in larvae exhibiting minor pronephric phenotypes. In conclusion, the developed workflow enables rapid and semi-automated in vivo assessment of multiple morphological and functional parameters.

Retinal perfusion 6months after trabeculectomy as measured by optical coherence tomography angiography.

To investigate potential changes of vessel density (VD) at the optic nerve head (ONH) and the macula 6months after trabeculectomy (TE). In a prospective monocentric study, 19 eyes with open-angle glaucoma were treated with TE + MMC (mitomycin C). At four different time points multiple morphological papillary parameters were measured by OCT, and the ONH VD in the radial peripapillary capillary layer and the superficial and deep plexuses of the macula was determined by OCTA (optical coherence tomography angiography, RTVue-XR, Optovue). The mean defect was determined by visual field examination (mode 30-2, Humphrey Field Analyzer). The duration of follow-up was 6months. Nineteen eyes, one each from 19 patients (11 females; 8 males) with a mean age of 66.0 (58.07, 70.94) years and a mean intraocular pressure (IOP) of 21.0mmHg (17.07, 23.87), were included in the study. All showed a significant reduction in IOP at each follow-up after TE (p < 0.0001). There was no significant change in the peripapillary retinal nerve fiber layer thickness (p = 0.88), the ganglion cell complex (p = 0.97), the cup-disk ratio (p = 0.63), the rim area (p = 0.78), or the mean visual field defect (p = 0.82). With regard to VD, no significant difference could be determined in either the ONH or the macular area. After significant surgical reduction of IOP by TE, there are no significant detectable morphological changes in the ONH or the ganglion cell complex as measured by OCT, nor does the papillary or macular OCTA-determined VD change significantly. Trial registration 2016-409-f-S Avanti-OCT-A. Registered December 1, 2016.