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Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the coronary band of the foot. It causes significant animal welfare and economic losses to the cattle industry. Early detection of DD can lead to prompt treatment and decrease lameness. Current detection and staging methods require a trained individual to evaluate the interdigital space on each foot for clinical signs of DD. Computer vision (CV), a type of artificial intelligence for image analysis, has demonstrated promising results on object detection tasks. However, farms require robust solutions that can be deployed in harsh conditions including dust, debris, humidity, precipitation, other equipment issues. The study aims to train, deploy, and benchmark DD detection models on edge devices. Images were collected from commercial dairy farms with the camera facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system. Models were trained to detect and score DD lesions and embedded on an edge device. The Tiny YOLOv4 model deployed on a CV specific integrated camera module connected to a single board computer achieved a mean average precision (mAP) of 0.895, an overall prediction accuracy of 0.873, and a Cohen's kappa of 0.830 for agreement between the computer vision model and the trained investigator. The model reached a final inference speed of 40 frames per second (FPS) and ran stably without any interruptions. The CV model was able to detect DD lesions on an edge device with high performance and speed. The CV tool can be used for early detection and prompt treatment of DD in dairy cows. Real-time detection of DD on edge device will improve health outcomes, while simultaneously decreasing labor costs. We demonstrate that the deployed model can be a low-power and portable solution for real-time detection of DD on dairy farms. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time detection of health outcomes in precision farming., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Transformer is widely used in medical image segmentation tasks due to its powerful ability to model global dependencies. However, most of the existing transformer-based methods are two-dimensional networks, which are only suitable for processing two-dimensional slices and ignore the linguistic association between different slices of the original volume image blocks. To solve this problem, we propose a novel segmentation framework by deeply exploring the respective characteristic of convolution, comprehensive attention mechanism, and transformer, and assembling them hierarchically to fully exploit their complementary advantages. Specifically, we first propose a novel volumetric transformer block to help extract features serially in the encoder and restore the feature map resolution to the original level in parallel in the decoder. It can not only obtain the information of the plane, but also make full use of the correlation information between different slices. Then the local multi-channel attention block is proposed to adaptively enhance the effective features of the encoder branch at the channel level, while suppressing the invalid features. Finally, the global multi-scale attention block with deep supervision is introduced to adaptively extract valid information at different scale levels while filtering out useless information. Extensive experiments demonstrate that our proposed method achieves promising performance on multi-organ CT and cardiac MR image segmentation.

The recent advent of whole slide imaging (WSI) systems has moved digital pathology closer to diagnostic applications and clinical practices. Integrating WSI with machine learning promises the growth of this field in upcoming years. Here we report the design and implementation of a handheld, colour-multiplexed, and AI-powered ptychographic whole slide scanner for digital pathology applications. This handheld scanner is built using low-cost and off-the-shelf components, including red, green, and blue laser diodes for sample illumination, a modified stage for programmable sample positioning, and a synchronized image sensor pair for data acquisition. We smear a monolayer of goat blood cells on the main sensor for high-resolution lensless coded ptychographic imaging. The synchronized secondary sensor acts as a non-contact encoder for precisely tracking the absolute object position for ptychographic reconstruction. For WSI, we introduce a new phase-contrast-based focus metric for post-acquisition autofocusing of both stained and unstained specimens. We show that the scanner can resolve the 388-nm linewidth on the resolution target and acquire gigapixel images with a 14 mm × 11 mm area in ∼70 seconds. The imaging performance is validated with regular stained pathology slides, unstained thyroid smears, and malaria-infected blood smears. The deep neural network developed in this study further enables high-throughput cytometric analysis using the recovered complex amplitude. The reported do-it-yourself scanner offers a portable solution to transform the high-end WSI system into one that can be made widely available at a low cost. The capability of high-throughput quantitative phase imaging may also find applications in rapid on-site evaluations.

Chromatic aberration is a major challenge faced by metalenses. Current methods to achieve broadband achromatic operation in metalenses usually suffer from limited size, numerical aperture, and working bandwidth due to the finite group delay of meta-atoms, thus restricting the range of practical applications. Multiwavelength achromatic metalenses can overcome those limitations, making it possible to realize larger numerical aperture (NA) and sizes simultaneously. However, they usually require three layers, which increases their fabrication complexity, and have only been demonstrated in small sizes, with low numerical aperture and modest efficiencies. Here, we demonstrate a 1 mm diameter red-green-blue achromatic metalens doublet with a designed NA of 0.8 and successfully apply the metalens in a digital imaging system. This work shows the potential of the doublet metasurfaces, extending their applications to digital imaging systems such as digital projectors, virtual reality glasses, high resolution microscopies, etc.

In recent years, the performance of sports dance in China has become better and better. Naturally, the technical requirements for this dance are getting higher and higher, and the number and intensity of training have also increased, which has led to increasing injuries in sports dance. This article is based on visual sensor images to analyze and study the common injuries and prevention of sports dance practitioners. It is aimed at providing a certain reference basis for athletes' injuries, so that dance practitioners and coaches can better master sports dance training and teaching. Injury-related rules and prevention reduce the injury rate. This article puts forward the related technology of a visual sensor image and applies its technology to the prevention and research of common injuries in sports dance. At the same time, it analyzes the causes of sports dance practitioners' injuries and seeks economical and affordable massage techniques for prevention, and the method of treatment provides protection for dance practitioners. The experimental results in this article show that the Tuina group cured 15 subjects, 41 subjects were markedly effective, 13 subjects were improved, and 6 subjects were unhealed. The total effective rate was 92%., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Chengxuan Yu and Ru Li.)

Background: Although many cervical cytology diagnostic support systems have been developed, it is challenging to classify overlapping cell clusters with a variety of patterns in the same way that humans do. In this study, we developed a fast and accurate system for the detection and classification of atypical cell clusters by using a two-step algorithm based on two different deep learning algorithms., Methods: We created 919 cell images from liquid-based cervical cytological samples collected at Sapporo Medical University and annotated them based on the Bethesda system as a dataset for machine learning. Most of the images captured overlapping and crowded cells, and images were oversampled by digital processing. The detection system consists of two steps: (1) detection of atypical cells using You Only Look Once v4 (YOLOv4) and (2) classification of the detected cells using ResNeSt. A label smoothing algorithm was used for the dataset in the second classification step. This method annotates multiple correct classes from a single cell image with a smooth probability distribution., Results: The first step, cell detection by YOLOv4, was able to detect all atypical cells above ASC-US without any observed false negatives. The detected cell images were then analyzed in the second step, cell classification by the ResNeSt algorithm, which exhibited average accuracy and F-measure values of 90.5% and 70.5%, respectively. The oversampling of the training image and label smoothing algorithm contributed to the improvement of the system's accuracy., Conclusion: This system combines two deep learning algorithms to enable accurate detection and classification of cell clusters based on the Bethesda system, which has been difficult to achieve in the past. We will conduct further research and development of this system as a platform for augmented reality microscopes for cytological diagnosis., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter., Competing Interests: Declaration of Competing Interest FS is an inventor on a patent related to the study., (Copyright © 2021. Published by Elsevier Inc.)

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases-caused by site differences in scanner/image-acquisition protocols-negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling-subject (TS) dataset including 56 T1-weighted MRI scans of 20 healthy participants in three different MRI procedures-20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively-was considered to compare the reproducibility of TS-GLM, ComBat, and TS-ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement-bias indicator. The measurement-bias reduction realized with different methods was evaluated by comparing test-retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test-retest datasets. The results revealed that TS-GLM and TS-ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS-based harmonization outperforms ComBat for measurement-bias reduction and is optimal for MRI data in well-prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large-scale study is an interesting future problem., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Microscopic observation of biological specimen smears is the mainstay of diagnostic pathology, as defined by the Digital Pathology Association. Though automated systems for this are commercially available, their bulky size and high cost renders them unusable for remote areas. The research community is investing much effort towards building equivalent but portable, low-cost systems. An overview of such research is presented here, including a comparative analysis of recent reports. This paper also reviews recently reported systems for automated staining and smear formation, including microfluidic devices; and optical and computational automated microscopy systems including smartphone-based devices. Image pre-processing and analysis methods for automated diagnosis are also briefly discussed. It concludes with a set of foreseeable research directions that could lead to affordable, integrated and accurate whole slide imaging systems., (© 2021 Royal Microscopical Society.)

Rapid autofocusing over long distances is critical for tracking 3D topological variations and sample motion in real time. Taking advantage of a deformable mirror and Shack-Hartmann wavefront sensor, remote focusing can permit fast axial scanning with simultaneous correction of system-induced aberrations. Here, we report an autofocusing technique that combines remote focusing with sequence-dependent learning via a bidirectional long short term memory network. A 120 µm autofocusing range was achieved in a compact reflectance confocal microscope both in air and in refractive-index-mismatched media, with similar performance under arbitrary-thickness liquid layers up to 1 mm. The technique was validated on sample types not used for network training, as well as for tracking of continuous axial motion. These results demonstrate that the proposed technique is suitable for real-time aberration-free autofocusing over a large axial range, and provides unique advantages for biomedical, holographic and other related applications.

Waves on the surface of developing eggs/embryos need to be viewed from all sides of their 3D tissue. The ball microscope will enable tracking of cellular waves and determine their interactions with the cells on the surface. Nine microscopes are arrayed in a spherical formation around an imaging stage to create whole surface images of objects anywhere from 0.5 mm 3 to 60 mm 3 in size. The 3D printed ball-based microscope is made using nine, Opti-Tekscope OT-HD Digital USB Microscope Camera Magnifiers. Eight of the microscope cameras fit into the ball at 90° angles to each other and one bottom microscope is used for a base to hold the stage. The base will support a customised cuvette to hold the embryo in water. The microscopes are the size of a pen (13 cm long and 1 cm in diameter) and each have a ring light around their diameter for self illumination. The nine microscopes can be attached to a microcontroller for time-lapse automated imaging. This microscope will be compared to other microscopes developed for the same purpose. The microscope can be used for time lapse imaging of the surface of small 3D objects and can be used to view Axolotl salamander embryo development as the Axolotl embryos are 2 mm in diameter. Other amphibian eggs can also be imaged using this technique., (Copyright © 2021 Elsevier B.V. All rights reserved.)

We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labour. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists. LAY DESCRIPTION: Electron tomography of biological samples is used for a three-dimensional (3D) reconstruction of filamentous structures, such as microtubules (MTs) in mitotic and meiotic spindles. Large-scale electron tomography can be applied to increase the reconstructed volume for the visualisation of full spindles. For this, each spindle is cut into a series of semi-thick physical sections, from which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. Previously, we presented fully automatic approaches for this 3D reconstruction pipeline. However, large volumes often suffer from imperfections (ie physical distortions) caused by the image acquisition process, making it difficult to apply fully automatic approaches for matching and stitching of numerous tomograms. Therefore, we developed an interactive, semi-automatic solution that considers all requirements for large-scale stitching of microtubules in image stacks of consecutive sections. We achieved this by combining automatic methods, visual validation and interactive error correction, thus allowing the user to continuously improve the result by interactively correcting landmarks or matches of filaments. We present large-scale reconstructions of spindles in which the automatic workflow failed and where different steps of manual corrections were needed. Our approach is also applicable to other biological samples showing 3D distributions of MTs in a number of different cellular contexts., (© 2021 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)

Background: Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear., Methods: For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach., Findings: 50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques., Interpretation: Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services., Funding: None., Competing Interests: Declaration of interests AA reports consulting fees for Olympus and Medtronic. SC reports consulting fees for Olympus and Medtronic. AF reports consulting fees for Olympus. AR reports consulting fees for Fuji, Olympus, and Medtronic. CH reports consulting fees for Fuji and Medtronic. RM reports consulting fees for Fuji. DKR reports consulting fees for Olympus, Boston, Aries Pharmaceutical, Braintree Laboratories, Norgine, Endokey, GI Supply, and Medtronic. All other authors declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation., (© 2021. The Author(s).)

Context.—: Pathology practices have begun integrating digital pathology tools into their routine workflow. During 2020, the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a pandemic, causing a global health crisis that significantly affected the world population in several areas, including medical practice, and pathology was no exception., Objective.—: To summarize our experience in implementing digital pathology for remote primary diagnosis, education, and research during this pandemic., Design.—: We surveyed our pathologists (all subspecialized) and trainees to gather information about their use of digital pathology tools before and during the pandemic. Quality assurance and slide distribution data were also examined., Results.—: During the pandemic, the widespread use of digital tools in our institution allowed a smooth transition of most clinical and academic activities into remote with no major disruptions. The number of pathologists using whole slide imaging (WSI) for primary diagnosis increased from 20 (62.5%) to 29 (90.6%) of a total of 32 pathologists, excluding renal pathology and hematopathology, during the pandemic. Furthermore, the number of pathologists exclusively using whole slide imaging for primary diagnosis also increased from 2 (6.3%) to 5 (15.6%) during the pandemic. In 35 (100%) survey responses from attending pathologists, 21 (60%) reported using whole slide imaging for remote primary diagnosis following the Centers for Medicare and Medicaid Services waiver. Of these 21 pathologists, 18 (86%) responded that if allowed, they will continue using whole slide imaging for remote primary diagnosis after the pandemic., Conclusions.—: The pandemic served as a catalyst to pathologists adopting a digital workflow into their daily practice and realizing the logistic and technical advantages of such tools.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources., (© 2021. The Author(s).)

Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting these traits' genetic architectures toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain as challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users' experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crop species. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree)., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

X-ray propagation-based imaging techniques are well established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase-retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and diffusion of such techniques difficult. Here, PyPhase, a free and open-source package for propagation-based near-field phase reconstructions, which is distributed under the CeCILL license, is presented. PyPhase implements some of the most popular phase-retrieval algorithms in a highly modular framework supporting its deployment on large-scale computing facilities. This makes the integration, the development of new phase-retrieval algorithms, and the deployment on different computing infrastructures straightforward. Its capabilities and simplicity are presented by application to data acquired at the synchrotron source MAX IV (Lund, Sweden)., (open access.)

Quantitative phase imaging using holographic microscopy is a powerful and non-invasive imaging method, ideal for studying cells and quantifying their features such as size, thickness, and dry mass. However, biological materials scatter little light, and the resulting low signal-to-noise ratio in holograms complicates any downstream feature extraction and hence applications. More specifically, unwrapping phase maps from noisy holograms often fails or requires extensive computational resources. We present a strategy for overcoming the noise limitation: rather than a traditional phase-unwrapping method, we extract the continuous phase values from holograms by using a phase-generation technique based on conditional generative adversarial networks employing a Pix2Pix architecture. We demonstrate that a network trained on random surfaces can accurately generate phase maps for test objects such as dumbbells, spheres, and biconcave discoids. Furthermore, we show that even a rapidly trained network can generate faithful phase maps when trained on related objects. We are able to accurately extract both morphological and quantitative features from the noisy phase maps of human leukemia (HL-60) cells, where traditional phase unwrapping algorithms fail. We conclude that deep learning can decouple noise from signal, expanding potential applications to real-world systems that may be noisy.

Purpose: To determine which sequence for frequently used general contrast-enhanced brain MRI shows the least radiofrequency shielding effect of a titanium mesh in cranioplasty using a phantom., Methods: A 1.5T MRI scanner was used. Frequently used general 2D and 3D spin-echo sequences (SE) and T 1 spoiled gradient echo sequences (GRE) used for MRI in clinical settings were adopted in this study. A titanium mesh was placed above a cubic phantom containing manganese chloride tetrahydrate and sodium chloride. The signal attenuation ratio and normalized absolute average deviation (NAAD) were calculated. Moreover, the flip angle (FA) dependency in SE and area of excitation dependency in 3D sequences were analyzed using NAAD., Results: The signal attenuation ratio at the position nearest to the titanium mesh for 2D SE was 71.8% larger than that at the position nearest to the titanium mesh for 3D GRE. With regard to NAAD, 3D GRE showed the highest values among the sequences. When FA was increased, radiofrequency shielding effect was improved. There were no significant differences between the narrow and wide area of excitation. 3D GRE showed the least radiofrequency shielding effect, and it was considered as the optimal sequence for MRI in the presence of a titanium mesh., Conclusion: 3D GRE shows the least radiofrequency shielding effect of a titanium mesh after cranioplasty among frequently used general sequences for contrast-enhanced brain MRI.

The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of the assay, including publication of a standard protocol and extensive validation. However, the diverse morphology of skin cells makes cell preparation and scoring of micronuclei (MN) tedious and subjective, thus requiring a high level of technical expertise for evaluation. This ultimately has a negative impact on throughput and the assay would benefit by the development of an automated method which could reduce scoring subjectivity while also improving the robustness of the assay by increasing the number of cells that can be scored. Imaging flow cytometry (IFC) with the ImageStream ®X Mk II can capture high-resolution transmission and fluorescent imagery of cells in suspension. This proof-of-principle study describes protocol modifications that enable such automated measurement in 3D skin cells following exposure to mitomycin C and colchicine. IFC was then used for automated image capture and the Amnis® Artificial Intelligence (AAI) software permitted identification of binucleated (BN) cells with 91% precision. On average, three times as many BN cells from control samples were evaluated using IFC compared to the standard manual analysis. When IFC MNBN cells were visually scored from within the BN cell images, their frequency compared well with manual slide scoring, showing that IFC technology can be applied to the RSMN assay. This method enables faster time to result than microscope-based scoring and the initial studies presented here demonstrate its capability for the detection of statistically significant increases in MNBN frequencies. This work therefore demonstrates the feasibility of combining IFC and AAI to automate scoring for the RSMN assay and to improve its throughput and statistical robustness., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze., Competing Interests: CW, TM, VV, KY, MT, KY, KO, YF, TT, TI, KA, TN, EH, KK No competing interests declared, (© 2021, Wen et al.)

In different mechanical conditions, repetitive friction in combination with pressure, shear, temperature, and moisture leads to skin discomfort and imposes the risks of developing skin injuries such as blisters and pressure ulcers, frequently reported in athletes, military personnel, and in people with compromised skin conditions and/or immobility. Textiles next to skin govern the skin microclimate, have the potential to influence the mechanical contact with skin, and contribute to skin comfort and health. The adhesion-friction theory suggests that contact area is a critical factor to influence adhesion, and therefore, friction force. Friction being a surface phenomenon, most of the studies concentrated on the surface profile or topographic analysis of textiles. This study investigated both the surface profiles and the inner construction of the fabrics through X-ray microcomputed tomographic three-dimensional image analysis. A novel nondestructive method to evaluate yarn and fabric structural details quantitatively and calculate contact area (in fiber area %) experimentally has been reported in this paper. Plain and satin-woven fabrics with different thread densities and made from 100% cotton ring-spun yarns with two different linear densities (40 and 60 Ne) were investigated in this study. The measurements from the tomographic images (pixel size: 1.13 μm) and the fiber area % analysis were in good agreement to comprehend and compare the yarn and fabric properties reported. The fiber area % as reported in this paper can be used to evaluate the skin-textile interfaces and quantitatively determine the contact area under different physical, mechanical, and microclimatic conditions to understand the actual skin-textile interaction during any physical activity or sports. The proposed method can be helpful in engineering textiles to enhance skin comfort and prevent injuries, such as blisters and pressure ulcers, in diversified application areas, including but not limited to, sports and healthcare apparel, military apparel, and firefighter's protective clothing. In addition, the images were capable of precisely evaluating yarn diameters, crimp %, and packing factor as well as fabric thickness, volumetric densities, and cover factors as compared with those obtained from theoretical evaluation and existing classical test methods. All these findings suggest that the proposed new method can reliably be used to quantify the yarn and fabric characteristics, compare their functionality, and understand the structural impacts in an objective and nondestructive way.

Functional neuroimaging using MRI relies on measurements of blood oxygen level-dependent (BOLD) signals from which inferences are made about the underlying neuronal activity. This is possible because neuronal activity elicits increases in blood flow via neurovascular coupling, which gives rise to the BOLD signal. Hence, an accurate interpretation of what BOLD signals mean in terms of neural activity depends on a full understanding of the mechanisms that underlie the measured signal, including neurovascular and neurometabolic coupling, the contribution of different cell types to local signalling, and regional differences in these mechanisms. Furthermore, the contributions of systemic functions to cerebral blood flow may vary with ageing, disease and arousal states, with regard to both neuronal and vascular function. In addition, recent developments in non-invasive imaging technology, such as high-field fMRI, and comparative inter-species analysis, allow connections between non-invasive data and mechanistic knowledge gained from invasive cellular-level studies. Considered together, these factors have immense potential to improve BOLD signal interpretation and bring us closer to the ultimate purpose of decoding the mechanisms of human cognition. This theme issue covers a range of recent advances in these topics, providing a multidisciplinary scientific and technical framework for future work in the neurovascular and cognitive sciences. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Fluorescence microscopy has become an indispensable tool for cell biology. Recently, super-resolution methods have been developed to overcome the diffraction limit of light and have shown living cells in unprecedented detail. Often, these methods come at a high cost and with complexity in terms of instrumentation and sample preparation, thus calling for the development of low-cost, more accessible methods. We previously developed image scanning microscopy (ISM), which uses structured illumination to double the resolution and quadruple the contrast of a confocal microscope. Implementing this technique into a confocal spinning-disk (CSD) microscope allows recording ISM images with up to ~1 frame per second, making it ideal for imaging dynamic biological processes. Here we present a step-by-step protocol describing how to convert any existing commercial CSD microscope into a CSD-ISM, with only moderate changes to the hardware and at low cost. Operation of the CSD-ISM is realized with a field programmable gate array using the software environment Micro-Manager, a popular open-source platform for microscopy. The provided software ( https://projects.gwdg.de/projects/csdism-2020 ) takes care of all algorithmic complexities and numerical workload of the CSD-ISM, including hardware synchronization and image reconstruction. The hardware modifications described here result in a theoretical maximum increase in resolution of √2 ≈ 1.41, which can be further improved by deconvolution to obtain a theoretical maximum twofold increase. An existing CSD setup can be upgraded in ~3 d by anyone with basic knowledge in optics, electronics and microscopy software.

Recently, there has been an explosion of scientific literature describing the use of colorimetry for monitoring the progression or the endpoint result of colorimetric reactions. The availability of inexpensive imaging technology (e.g., scanners, Raspberry Pi, smartphones and other sub-$50 digital cameras) has lowered the barrier to accessing cost-efficient, objective detection methodologies. However, to exploit these imaging devices as low-cost colorimetric detectors, it is paramount that they interface with flexible software that is capable of image segmentation and probing a variety of color spaces (RGB, HSB, Y'UV, L*a*b*, etc.). Development of tailor-made software (e.g., smartphone applications) for advanced image analysis requires complex, custom-written processing algorithms, advanced computer programming knowledge and/or expertise in physics, mathematics, pattern recognition and computer vision and learning. Freeware programs, such as ImageJ, offer an alternative, affordable path to robust image analysis. Here we describe a protocol that uses the ImageJ program to process images of colorimetric experiments. In practice, this protocol consists of three distinct workflow options. This protocol is accessible to uninitiated users with little experience in image processing or color science and does not require fluorescence signals, expensive imaging equipment or custom-written algorithms. We anticipate that total analysis time per region of interest is ~6 min for new users and <3 min for experienced users, although initial color threshold determination might take longer.

Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells-a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Objective: 18 F is the most extensively used radioisotope in current clinical practices of PET imaging. This selection is based on the several criteria of pure PET radioisotopes with an optimum half-life, and low positron energy that contributes to a smaller positron range. In addition to 18 F, other radioisotopes such as 68 Ga and 124 I are currently gained much attention with the increase in interest in new PET tracers entering the clinical trials. This study aims to determine the minimal scan time per bed position (T min ) for the 124 I and 68 Ga based on the quantitative differences in PET imaging of 68 Ga and 124 I relative to 18 F., Methods: The European Association of Nuclear Medicine (EANM) procedure guidelines version 2.0 for FDG-PET tumor imaging has adhered for this purpose. A NEMA2012/IEC2008 phantom was filled with tumor to background ratio of 10:1 with the activity concentration of 30 kBq/ml ± 10 and 3 kBq/ml ± 10% for each radioisotope. The phantom was scanned using different acquisition times per bed position (1, 5, 7, 10 and 15 min) to determine the T min . The definition of T min was performed using an image coefficient of variations (COV) of 15%., Results: T min obtained for 18 F, 68 Ga and 124 I were 3.08, 3.24 and 32.93 min, respectively. Quantitative analyses among 18 F, 68 Ga and 124 I images were performed. Signal-to-noise ratio (SNR), contrast recovery coefficients (CRC), and visibility (V H ) are the image quality parameters analysed in this study. Generally, 68 Ga and 18 F gave better image quality as compared to 124 I for all the parameters studied., Conclusion: We have defined T min for 18 F, 68 Ga and 124 I SPECT CT imaging based on NEMA2012/IEC2008 phantom imaging. Despite the long scanning time suggested by T min , improvement in the image quality is acquired especially for 124 I. In clinical practice, the long acquisition time, nevertheless, may cause patient discomfort and motion artifact.

This study was conducted to determine optimum image capture frame rates (FRO) when there was evaluation of different types of counting chambers used for CASA-Mot determinations of stallion sperm motility. Sperm VCL was determined at frame rates of 25-250 f/s in: 1) Spermtrack® (Spk) 10 and 20 chambers (drop displacement-type chambers 10 and 20 μm-deep respectively; and 2) ISAS®D4C10, ISAS®D4C20 (10 and 20 μm-deep respectively) and ISAS®D4C20 L (20 μm-deep) capillary loaded chambers. Values for different sperm kinematic variables were determined using each chamber at 250 f/s, which is the maximum frame rate that the software can be used for analyses. With evaluation of Spk chambers, there was a greater curvilinear velocity (VCL), average path velocity (VAP), straight line velocity (STR), amplitude of lateral head displacement (ALH) and beat cross frequency (BCF) values (P < 0.05) than with capillary loaded chambers, with there being greatest values with 20 μm-deep chambers. With the Spk10 chamber, VCL and ALH were greater at the chamber centre than periphery. There were no such differences for the Spk20 chamber. With evaluation of the D4C10 chamber, VSL and STR were less when there was a sperm deposition point towards the chamber end, while there were the opposite for the D4C20 chamber. When there was evaluation of the D4C20 chamber, there were also greater VCL, WOB and BCF values in distal areas. With use of most of these chambers, data should be collected from different fields and means determined, however, this is not necessary with Spk20 chambers., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A 1225-Channel Neuromorphic Retinal Prosthesis (RP) SoC is presented. Existing RP SoCs directly convert light intensity to electrical stimulus, which limit the adoption of delicate stimulus patterns to increase visual acuity. Moreover, a conventional centralized image processor leads to the local hot spot that poses a risk to the nearby retinal cells. To solve these issues, the proposed SoC adopts a distributed Neuromorphic Image Processor (NMIP) located within each pixel that extracts the outline of the incoming image, which reduces current dispersion and stimulus power compared with light-intensity proportional stimulus pattern. A spike-based asynchronous digital operation results in the power consumption of 56.3 nW/Ch without local temperature hot spot. At every 5×5 pixels, the localized (49-point) temperature-regulation circuit limits the temperature increase of neighboring retinal cells to less than 1 °C, and the overall power consumption of the SoC to be less than that of the human eye. The 1225-channel SoC fabricated in 0.18 μm 1P6M CMOS occupies 15mm 2 while consuming 2.7 mW, and is successfully verified with image reconstruction demonstration.

Purpose: To compare, vs CMR, four softwares: quantitative gated SPECT (QGS), myometrix (MX), corridor 4DM (4DM), and Emory toolbox (ECTb) to evaluate left ventricular ejection fraction (LVEF), end-systolic (ESV), and end-diastolic volumes (EDVs) by gated MPI CZT-SPECT., Methods: 48 patients underwent MPI CZT-SPECT and CMR 6 weeks after STEMI, LV parameters were measured with four softwares at MPI CZT-SPECT vs CMR. We evaluated (i) concordance and correlation between MPI CZT-SPECT and CMR, (ii) concordance MPI CZT-SPECT/CMR for the categorical evaluation of the left ventricular dysfunction, and (iii) impacts of perfusion defects > 3 segments on concordance., Results: LVEF: LCC QGS/CMR = 0.81 [+ 2.2% (± 18%)], LCC MX/CMR = 0.83 [+ 1% (± 17.5%)], LCC 4DM/CMR = 0.73 [+ 3.9% (± 21%)], LCC ECTb/CMR = 0.69 [+ 6.6% (± 21.1%)]. ESV: LCC QGS/CMR = 0.90 [- 8 mL (± 40 mL)], LCC MX/CMR = 0.90 [- 9 mL (± 36 mL)], LCC 4DM/CMR = 0.89 [+ 4 mL (± 45 mL)], LCC ECTb/CMR = 0.87 [- 3 mL (± 45 mL)]. EDV: LCC QGS/CMR = 0.70 [- 16 mL (± 67 mL)], LCC MX/CMR = 0.68 [- 21 mL (± 63 mL], LCC 4DM/CMR = 0.72 [+ 9 mL (± 73 mL)], LCC ECTb/CMR = 0.69 [+ 10 mL (± 70 mL)]., Conclusion: QGS and MX were the two best-performing softwares to evaluate LVEF after recent STEMI.

It is well recognized that isolated cardiac muscle cells beat in a periodic manner. Recently, evidence indicates that other, non-muscle cells, also perform periodic motions that are either imperceptible under conventional lab microscope lens or practically not easily amenable for analysis of oscillation amplitude, frequency, phase of movement and its direction. Here, we create a real-time video analysis tool to visually magnify and explore sub-micron rhythmic movements performed by biological cells and the induced movements in their surroundings. Using this tool, we suggest that fibroblast cells perform small fluctuating movements with a dominant frequency that is dependent on their surrounding substrate and its stiffness., Competing Interests: The authors have declared that no competing interests exist.

Objective: To investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome., Methods: The statistical reliability of radiomic features was assessed retrospectively in three clinical datasets (patient numbers: 108 head and neck cancer, 37 small-cell lung cancer, 47 non-small-cell lung cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX). PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effects of IBSI compliance, user-defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable cox regression in the largest dataset., Results: The reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios., Conclusion: IBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics., Key Points: • Reliability of radiomic features varies between feature calculation platforms and with choice of software version. • Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised. • IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.

Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand-face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people's behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen's Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.

We aimed to investigate the image quality of the U-SPECT5/CT E-Class a micro single-photon emission computed tomography (SPECT) system with two large stationary detectors for visualization of rat hearts and bones using clinically available 99m Tc-labelled tracers. Sensitivity, spatial resolution, uniformity and contrast-to-noise ratio (CNR) of the small-animal SPECT scanner were investigated in phantom studies using an ultra-high-resolution rat and mouse multi-pinhole collimator (UHR-RM). Point source, hot-rod, and uniform phantoms with 99m Tc-solution were scanned for high-count performance assessment and count levels equal to animal scans, respectively. Reconstruction was performed using the similarity-regulated ordered-subsets expectation maximization (SROSEM) algorithm with Gaussian smoothing. Rats were injected with ~ 100 MBq [ 99m Tc]Tc-MIBI or ~ 150 MBq [ 99m Tc]Tc-HMDP and received multi-frame micro-SPECT imaging after tracer distribution. Animal scans were reconstructed for three different acquisition times and post-processed with different sized Gaussian filters. Following reconstruction, CNR was calculated and image quality evaluated by three independent readers on a five-point scale from 1 = "very poor" to 5 = "very good". Point source sensitivity was 567 cps/MBq and radioactive rods as small as 1.2 mm were resolved with the UHR-RM collimator. Collimator-dependent uniformity was 55.5%. Phantom CNR improved with increasing rod size, filter size and activity concentration. Left ventricle and bone structures were successfully visualized in rat experiments. Image quality was strongly affected by the extent of post-filtering, whereas scan time did not have substantial influence on visual assessment. Good image quality was achieved for resolution range greater than 1.8 mm in bone and 2.8 mm in heart. The recently introduced small animal SPECT system with two stationary detectors and UHR-RM collimator is capable to provide excellent image quality in heart and bone scans in a rat using standardized reconstruction parameters and appropriate post-filtering. However, there are still challenges in achieving maximum system resolution in the sub-millimeter range with in vivo settings under limited injection dose and acquisition time.

Three-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images only readily provide information for two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, this 2D segmentation can be computationally extruded into the third dimension. However, current methods typically make the simplifying assumption that cells are straight rods. Here, we report Pomegranate, a pipeline that performs the extrusion into 3D using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents radially symmetric cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as a macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other radially symmetric cell types.

Rationale: Adolescent wrist trauma can cause epiphyseal dysplasia and even distal radius deformity malunion. At present, there is no uniform treatment standard for the malunion of the distal radius of adolescents. Osteotomy and autologous bone grafting are currently one of the effective ways to treat the disease. We treated an adolescent patient with distal radius deformity malunion, and used this surgical method to treat the patient and achieved satisfactory results., Patient Concerns: A 16-year-old boy suffered from a serious distal radius deformity after trauma of the left wrist 8 years ago., Diagnoses: Physical examination, X-rays examination, high-resolution computed tomography scan, and 3-dimensional reconstruction images of the affected limb helped us diagnose the distal radius fracture malunion., Interventions: The fracture malunion was treated by osteotomy and autologous iliac bone grafting., Outcomes: At the 2-year follow-up, wrist flexion returned to 68°, wrist dorsiflexion to 55°, radial deviation to 14°, ulnar deviation to 12°, forearm pronation to 75°, supination to 67°. Grip strength increased to 35.1 kg after 2 years of operation, recovered to 87% of the uninjured side. Quick DASH score at 2-year follow-up was 9. No complication, such as nonunion or infection, was observed., Lessons: This rare case provides valuable insights for hand surgeons. High-resolution computed tomography scan and 3-dimensional reconstruction can help us effectively diagnose wrist diseases. Small lesions on the articular surface of the distal radius will change the position and function of the wrist joint, and cause traumatic arthritis of the wrist joint. Therefore, it is very important to reconstruct the normal structure of the distal radius articular surface. Osteotomy and autologous iliac bone grafting are effective treatments for serious distal radius fracture malunion in the adolescent patient. During the operation, care should be taken to protect the osteoepiphysis to avoid bone dysplasia.

The behavior and microscale processes associated with freely suspended organisms, along with sinking particles underlie key ecological processes in the ocean. Mechanistically studying such multiscale processes in the laboratory presents a considerable challenge for microscopy: how to measure single cells at microscale resolution, while allowing them to freely move hundreds of meters in the vertical direction? Here we present a solution in the form of a scale-free, vertical tracking microscope, based on a 'hydrodynamic treadmill' with no bounds for motion along the axis of gravity. Using this method to bridge spatial scales, we assembled a multiscale behavioral dataset of nonadherent planktonic cells and organisms. Furthermore, we demonstrate a 'virtual-reality system for single cells', wherein cell behavior directly controls its ambient environmental parameters, enabling quantitative behavioral assays. Our method and results exemplify a new paradigm of multiscale measurement, wherein one can observe and probe macroscale and ecologically relevant phenomena at microscale resolution. Beyond the marine context, we foresee that our method will allow biological measurements of cells and organisms in a suspended state by freeing them from the confines of the coverslip.

Anthrax is one of the important diseases in humans and animals, caused by the gram-positive bacteria spores called Bacillus anthracis. The disease is still one of the health problems of developing countries. Due to fatigue and decreased visual acuity, microscopic diagnosis of diseases by humans may not be of good quality. In this paper, for the first time, a system for automatic and rapid diagnosis of anthrax disease simultaneously with detection and segmentation of B. anthracis bacteria in microscopic images has been proposed based on artificial intelligence and deep learning techniques. Two important architectures of deep neural networks including UNet and UNet++ have been used for detection and segmentation of the most important component of the image i.e. bacteria. Automated detection and segmentation of B. anthracis bacteria offers the same level of accuracy as the human diagnostic specialist and in some cases outperforms it. Experimental results show that these deep architectures especially UNet++ can be used effectively and efficiently to automate B. anthracis bacteria segmentation of microscopic images obtained under different conditions. UNet++ produces outstanding results despite the many challenges in this field, such as high image dimension, image artifacts, object crowding, and overlapping. We conducted our experiments on a dataset prepared privately and achieved an accuracy of 97% and the dice score of 0.96 on the patch test images. It also tested on whole raw images and a recall of 98% and accuracy of 97% is achieved, which shows excellent performance in the bacteria segmentation task. The low cost and high speed of diagnosis and no need for a specialist are other benefits of the proposed system., Competing Interests: Declaration of Competing Interest I, Dr. Hossein Khosravi, on behalf of other authors of the paper entitled “Automatic Bacillus anthracis Bacteria Detection and Segmentation in Microscopic Images Using UNet++”, declare that we have no financial or personal relationships with other people or organizations that could inappropriately influence our work., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Aim: To report our experience on CT investigation of animal mummies, focusing on the practical and radiological aspects of the study, the acquisition parameters and the different reconstruction techniques., Materials and Methods: Thirteen mummies underwent CT examination on the same CT scanner (Siemens sensation) with the following acquisition parameters: 120 kV; 140 mAs; slice thickness: 1 mm; reconstruction interval: 0.7 mm; and rotation time: 0.75 s. All datasets were reconstructed with both bone and soft tissue algorithms and archived on our picture archiving and communication system using their catalogue number as an identifier. Images were then transferred on IntelliSpace Portal (Philips Healthcare) for post-processing multiplanar and 3D reconstructions. The acquired data were submitted to anthropological analysis., Results: CT enabled the identification of the bundles content: four cats with complete skeleton, one upper part of a cat mummy, one lower part of a cat mummy, one cat head with four cervical vertebrae, two crocodiles, two raptors, skeletons from one or more snakes and one mummy with dog appearance, containing long bones. All cats and hawks showed cervical fractures; in one cat, the skull was collapsed inwards, and in another cat, the head was turned backwards; one cat presented a skeleton more radiopaque than normal with evidence of cracks related to the use of the resins for mummification that were poured directly over the corpse., Conclusions: CT is a valuable noninvasive technique to study Egyptian mummies, enabling in-depth analysis while preserving the integrity of the mummy bundles, ensuring protection of a valuable archaeological resource.

Digital morphology hematology analyzers are becoming more prevalent in laboratories Aims: investigate practices and assess the benefits and limits of digital automated microscopy in hematology., Methods: questionnaire sent by e-mail in 2018 to French public and private laboratories., Results: out of 118 responses (56 private, 62 public), 117 participants had a CellaVision® microscope, 1 had a West Medica®. Practices were sometimes different, especially in the choice of smears to be digitized or for quality controls (16.1% had internal quality controls, 48.3% external quality controls); 62.1% never used the red blood cell (RBC) characterization tool; the number of cells counted varied from 100 to 400. The study reported a high rate of agreement for these benefits: traceability (95.7%), staff training (94.1%), eye strain (91.4%), risk of error (87.2%), time saving (83.6%). Among the disadvantages, apart from the inadequate search for platelets clumps (93.2%), the agreement rates were often lower: adaptation to digital images (61.2%), difficult assessment of atypical morphologies (49.6%) or RBC morphology (49.6%)., Conclusion: despite well-established benefits, standardization of practices and technical improvement are still needed.

Magnetic particle imaging (MPI) is a new medical imaging technique visualizing the concentration distribution of superparamagnetic nanoparticles used as tracer material. MPI is not yet in clinical routine, since one of the challenges is the upscaling of scanners. Typically, the magnetic fields of MPI scanners are generated electromagnetically, resulting in an immense power consumption but providing high flexibility in terms of adjusting the field strengths and very fast image acquisition rates. Permanent magnets provide high flux densities and do not need any power supply. However, the flux density is not adjustable, and a mechanical movement is slow compared to electromagnetically varying fields. The MPI scanner concept proposed here uses permanent magnets and provides high flexibility, with the possibility to choose between fast overview scanning and detailed image acquisition. By mechanical rotation of magnetic rings in Halbach array configuration, it is possible to adjust the field or gradient strengths. The latter allows for determining the spatial resolution and the size of the field of view. A continuous mechanical rotation defines the coarseness of the scanning trajectory and image acquisition rate. This concept provides a comparable flexibility, as an alternating magnetic field and an adjustable field gradient can be applied as known from electromagnetically driven MPI systems, and therefore yields high potential for an enlarged system. We present the idea of an arrangement of Halbach arrays and how to calculate the generated magnetic fields. Simulations for an exemplary geometry are provided to show the potential of the proposed setup.

Introduction: 3D-printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D-printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols., Methods: The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol., Results: Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P < 0.001). As a result, the SNR and CNR were significantly decreased (P < 0.001). For FBP, the highest noise with poor SNR and CNR was protocol D with 19.0 ± 1.6 HU, 18.9 ± 2.5 and 25.1 ± 3.6, respectively. For IR algorithm, the highest strength (AIDR3D strong ) yielded the lowest noise with excellent SNR and CNR., Conclusions: The use of IR algorithm and increasing its strengths have reduced noise significantly and thus increased the SNR and CNR when compared to FBP. Therefore, this integrated 3D-printed phantom approach could be used for dose optimisation study and image quality analysis in CCTA protocols., (© 2020 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)

The aim of this study was to compare computer assisted sperm analysis (CASA) results of frozen thawed bull semen using three different chambers. Sixty bull frozen semen samples were thawed (37°C; 30 sec), extended in PBS (30×106 spermatozoa/mL; 37°C) and incubated (37°C; 2 min). Each semen sample was analyzed by CASA [total motility, progressive (pro)/ non-progressive/rapid/medium/slow movement spermatozoa, VCL, VSL, VAP, ALH, BCF, LIN, STR, WOB and hyperactive spermatozoa] using three different chambers: a Makler® chamber (MC; 10 μm); a Leja 4 chamber slide (LC; 20 μm); and a Glass slide covered with a coverslip (GSC; 10.3 μm). The Makler chamber gave higher values compared to both the LC and GSC for almost all examined parameters. No systematic effect was evident between LC and GSC for VCL, VSL, VAP, LIN, STR, WOB, ALH, and BCF. Method agreement between MC and LC was generally moderate, between MC and GSC poor and between LC and GSC moderate to good. In general, narrower limits of agreement were found in samples with lower values. In conclusion, the CASA outcomes could be influenced by the analysis chambers. This finding should be taken into consideration when comparing results from different laboratories., (Copyright© by the Polish Academy of Sciences.)

Fast-scan cyclic voltammetry (FSCV) is widely used for in vivo detection of neurotransmitters, but identifying analytes, particularly mixtures, is difficult. Data analysis has focused on identifying dopamine from cyclic voltammograms, but it would be better to analyze all the data in the three-dimensional FSCV color plot. Here, the goal was to use image analysis-based analysis of FSCV color plots for the first time, specifically the structural similarity index (SSIM), to identify rapid neurochemical events. Initially, we focused on identifying spontaneous adenosine events, as adenosine cyclic voltammograms have a primary oxidation at 1.3 V and a secondary oxidation peak that grows in over time. Using SSIM, sample FSCV color plots were compared with reference color plots, and the SSIM cutoff score was optimized to distinguish adenosine. High-pass digital filtering was also applied to remove the background drift and lower the noise, which produced a better LOD. The SSIM algorithm detected more adenosine events than a previous algorithm based on current versus time traces, with 99.5 ± 0.6% precision, 95 ± 3% recall, and 97 ± 2% F 1 score ( n = 15 experiments from three researchers). For selectivity, it successfully rejected signals from pH changes, histamine, and H 2 O 2 . To prove it is a broad strategy useful beyond adenosine, SSIM analysis was optimized for dopamine detection and is able to detect simultaneous events with dopamine and adenosine. Thus, SSIM is a general strategy for FSCV data analysis that uses three-dimensional data to detect multiple analytes in an efficient, automated analysis.