Two-dimensional Imaging Techniques Research Articles

Three-dimensional atomic insights into the metal-oxide interface in Zr-ZrO2 nanoparticles

Metal-oxide interfaces with poor coherency have specific properties comparing to bulk materials and offer broad applications in heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and the limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Atoms at the interface have low structural ordering, low coordination, and elongated bond length. Moreover, we observe porous structures such as Zr vacancies and nano-pores, and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides, such as interfacial structures in semiconductor and atomic motion during oxidation process.

MRCNN: Multi-input residual convolution neural network for three-dimensional reconstruction of bubble flows from light field images

Accurate measurement of bubbles in air-water two-phase flows holds immense significance in the realm of thermal hydraulics assessments within nuclear reactors. Nevertheless, conventional bubble measurement techniques grapple with challenges encompassing system intricacy, limited real-time capabilities, and inaccuracies stemming from their inherent two-dimensional (2-D) nature. In response, we pioneered an innovative three-dimensional (3-D) analysis approach that leverages light field (LF) imaging diagnosis and deep learning algorithms. Unlike traditional 2-D reconstruction methods, our approach enables direct computation of bubble depth from LF images using digital refocusing technology. Following calibration, a seamless transformation is established between the camera coordinate system and the real-world coordinate system using a sharpness evaluation algorithm. This calibration process ensures precise alignment of refocused images with real-world positions. Subsequently, fully automated and highly accurate computations of bubble depth are realized from input images via the incorporation of a multi-input residual convolution neural network (MRCNN). The limitations of traditional two-dimensional imaging techniques are effectively addressed by this methodology, resulting in a reduction in measurement errors. The study confirms the feasibility of employing LF imaging diagnosis and deep learning algorithms for bubble measurements in an air-water two-phase flow, offering a significant improvement over traditional methods.

Two-Dimensional Mammography Imaging Techniques for Screening Women with Silicone Breast Implants: A Pilot Phantom Study.

This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce these techniques. Images were acquired on a single Hologic Selenia Dimensions® unit. The mammography of the left breast of a single clinical case was included. Three methods of image acquisition were identified. They were based on misused, recommended, and reference settings. In the clinical case, image criteria scoring and the signal-to-noise ratio on breast tissue (SNRBT) were determined for two 2D projections and compared between the three techniques. The phantom study first compared the reference and misused settings by varying the AEC sensor position and, second, the recommended settings with a reduced current-time product (mAs) setting that was 13% lower. The signal-difference-to-noise ratio (SDNR) and detectability indexes at 0.1 mm (d' 0.1 mm) and 0.25 mm (d' 0.25 mm) were automatically quantified using ATIA software. Average glandular dose (AGD) values were collected for each acquisition. A statistical analysis was performed using Kruskal-Wallis and corrected Dunn tests (p < 0.05). The SNRBT was 2.6 times lower and the AGD was -18% lower with the reference settings compared to the recommended settings. The SNRBT values increased by +98% with the misused compared to the recommended settings. The AGD increased by +79% with the misused settings versus the recommended settings. The median values of the reference settings were 5.8 (IQR 5.7-5.9), 1.2 (IQR 0.0), 7.0 (IQR 6.8-7.2) and 1.2 (IQR 0.0) mGy and were significantly lower than those of the misused settings (p < 0.03): 7.9 (IQR 6.1-9.7), 1.6 (IQR 1.3-1.9), 9.2 (IQR 7.5-10.9) and 2.2 (IQR 1.4-3.0) mGy for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. A comparison of the recommended and reduced settings showed a reduction of -6.1 ± 0.6% (p = 0.83), -7.7 ± 0.0% (p = 0.18), -6.4 ± 0.6% (p = 0.19) and -13.3 ± 1.1% (p = 0.53) for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. This study showed that the IAEA phantom could be used to reproduce the three techniques for acquiring 2D mammography images in the presence of breast implants for raising awareness and for educational purposes. It could also be used to evaluate and optimize the manufacturer's recommended settings.

Pulse-echo ultrasound attenuation tomography

Objective. We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this tissue property. Approach. At every location, we compute normalized cross-correlations between the beamformed images that are obtained from emissions at different steering angles. We demonstrate that their log-amplitudes provide the changes between attenuation-induced amplitude losses undergone by the different incident waves. This allows us to formulate a linear tomographic problem, which we efficiently solve via a Tikhonov-regularized least-squares approach. Main results. The performance of our tomography technique is first validated in numerical examples and then experimentally demonstrated in custom-made tissue-mimicking phantoms with inclusions of varying size, echogenicity, and attenuation. We show that this technique is particularly good at resolving lateral variations in tissue attenuation and remains accurate in media with varying echogenicity. Significance. Based on a similar principle, this method can be easily combined with computed ultrasound tomography in echo mode for speed-of-sound imaging, paving the way towards a multi-modal ultrasound tomography framework characterizing multiple acoustic tissue properties simultaneously.

Revealing the complexity of meniscus microvasculature through 3D visualization and analysis

Three-dimensional information is essential for a proper understanding of the healing potential of the menisci and their overall role in the knee joint. However, to date, the study of meniscal vascularity has relied primarily on two-dimensional imaging techniques. Here we present a method to elucidate the intricate 3D meniscal vascular network, revealing its spatial arrangement, connectivity and density. A polymerizing contrast agent was injected into the femoral artery of human cadaver legs, and the meniscal microvasculature was examined using micro-computed tomography at different levels of detail and resolution. The 3D vascular network was quantitatively assessed in a zone-base analysis using parameters such as diameter, length, tortuosity, and branching patterns. The results of this study revealed distinct vascular patterns within the meniscus, with the highest vascular volume found in the outer perimeniscal zone. Variations in vascular parameters were found between the different circumferential and radial meniscal zones. Moreover, through state-of-the-art 3D visualization using micro-CT, this study highlighted the importance of spatial resolution in accurately characterizing the vascular network. These findings, both from this study and from future research using this technique, improve our understanding of microvascular distribution, which may lead to improved therapeutic strategies.

Geophysical and Sedimentological Characterization of Oil Sand Deposit in Part of Eastern Dahomey Basin Southwestern Nigeria

Unconventional oil is the group name given to unconventional petroleum deposits such as oil sands and heavy oils. Oil sand, commonly referred to as tar sand or bituminous sand is a sandstone formation impregnated with viscous bitumen. This study utilized the two-dimensional electrical resistivity imaging technique to characterize oil sand and validate its true resistivity value and sedimentological analysis to evaluate the physical properties of the oil sand deposit. The two-dimension electrical resistivity imaging data acquisition was done along seven profiles systematically in a grid format using dipole-dipole electrode configuration and processed using the RES2DINV software. Fresh samples were collected from three points within the study area for grain sizes and textural properties analysis. The result of the 2-D ERI in the study area showed two to three geoelectric layers and the oil sand being the second layer as evident in the 3-D model obtained from the systemic data acquisition procedure. The resistivity obtained for oil sand in the study area range between 2,500 Ωm to 105 Ωm which validates the report of high resistivity value of oil sand in the study area. The sedimentological analysis showed that the sediments in the study area are of medium sand moderately sorted sand grain and leptokurtic to mesokurtic sand suggesting unimodal and bimodal parent source.

Microstructural changes in thermochemical heat storage material over cycles: Insights from micro-X-ray computed tomography

This paper studies the effect of successive (de)hydration cycles on the structure of potassium carbonate K2CO3·1.5H2O grains for low-temperature heat storage applications. Such structural changes are caused by exposure of the salt to water vapor or removal of water from it, accompanied by successive swelling and shrinkage. Understanding the material's internal structure is key to predicting its behaviour and optimizing its design. However, due to the simultaneous and persistent occurrence of structural changes and transport mechanisms throughout the process, gaining a complete understanding of the phenomenon can be challenging.Unlike conventional experimental approaches and two-dimensional imaging techniques used for porosity assessment, our study showcases the qualitative and quantitative alterations in the porosity and microstructure of potassium carbonate. This analysis is achieved by using Micro-X-ray computed tomography (Micro-CT). The study focuses on the impact of cycling on grain microstructure, investigating pore volume distribution, radial variation of pore sizes, and density of individual grains. It was noted that the porosity increased from 6.4 % to 19.7 % after seven cycles. Initially, we observed a greater number of pores in the core of the uncycled salt grain. However, after cycling, we noticed a more even distribution, with a higher number of pores in the outer region of the grain, which caused a radial change in porosity. Lastly, this research provides the intrinsic and apparent densities of both non-cycled and cycled potassium carbonate specimens.Micro-CT is a good tool for a better understanding of changes in thermochemical material at a structural level. Calculation of porosity provided a pathway to calculate apparent and intrinsic density. The demonstrated method can be used for a wide range of salt hydrates, enhancing the scope and applicability of this study in the field of low-temperature heat storage applications. Additionally, it gives the measuring parameter needed to calculate energy density and change in volume during the reaction.

CONE BEAM COMPUTED TOMOGRAPHY, IMPORTANCE OF EXAMINATION FOR THE STORAGE OF THERAPEUTICAL FAILURES IN IMPLANTOLOGY

CBCT (Cone Beam Computed Tomography) plays a crucial role in implantology, offering significant advantages over traditional two-dimensional imaging techniques, such as X-rays. Here are some of the important benefits of CBCT in implantology. Precise three-dimensional imaging: CBCT provides high-resolution three-dimensional images of the patient's oral and maxillofacial region. This allows a comprehensive assessment of anatomical structures, including bone volume, quality, and density. Precise evaluation of these factors is crucial for successful implant placement. In the analysis of CBCTs, we followed all the parameters of the hard tissues, and depending on them we opted for the insertion of the implants, ensuring their predictability post-resort through rigorous management of each clinical case. Using CBCT, the bone supply at the level of each implant site was examined, corresponding to each tooth at the jaw and mandibular level, subsequently performing a computerized simulation of implant positioning in relation to neighborhood anatomical formations, taking into account the indications of subsequent prosthetic rehabilitation, including as long as clinical cases have allowed patients' aesthetic desires. The CBCT-type investigation is indispensable for complex and complete implantology oral rehabilitation for each clinical case, taking into account, as far as possible, the guided insertion of implants from a prosthetic point of view.

Groundwater Investigation Using Electrical Resistivity Imaging Technique in Western Ramadi, Iraq

The two-dimensional electrical resistivity imaging technique was used to examine groundwater in western Ramadi. The survey was performed with a factor (n) and a-spacing values of 6 and 5 m, respectively. The inverse models clearly show the resistivity variation between the anomalous parts of the water table and all the sediments. The vertical extension of the saturation sediments is between 1.5–9.5 m, which is identical to the groundwater level in the three existing wells located in the University of Anbar, with an area of 7 kilometers and an area of 18 kilometers. The extreme thickness of the water saturation layer was determined to be about 10.5 meters. It was concentrated in gypsum and mudstone soils with secondary gypsum and siltstone, considered the upper components of the Injana Formation at the University of Anbar. At 7 Kilometer area which starts from a depth of 1 m to depths of up to 12 m, it is also considered a groundwater reservoir. While 18 Km area is considered to have dry soil and the groundwater in it is far from the surface at 12 m depth.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

A new neuroanatomical two-dimensional fitting three-dimensional imaging techniques in neuroanatomy education

BackgroundNeuroanatomy is the most abstract and complex anatomy. Neurosurgeons have to spend plenty of time mastering the nuances of the autopsy. However, the laboratory that can meet the requirements of neurosurgery microanatomy is only owned by several large medical colleges because it is an expensive affair. Thus, laboratories worldwide are searching for substitutes,but the reality and local details might not meet the exact requirements of the anatomical structure. Herein, we compared the traditional teaching mode, the 3D image generated by the current advanced hand-held scanner and our self-developed 2D image fitting 3D imaging method in the comparative study of neuroanatomy education.MethodsTo examine the efficacy of two-dimensional fitting three-dimensional imaging techniques in neuroanatomy education. 60 clinical students of grade 2020 in Wannan Medical College were randomly divided into traditional teaching group, hand held scanner 3D imaging group and 2D fitting 3D method group, with 20 students in each group.First, the modeling images of the hand held scanner 3D imaging group and the 2D fitting 3D method group are analyzed and compared, and then the teaching results of the three groups are evaluated by objective and subjective evaluation methods. The objective evaluation is in the form of examination papers, unified proposition and unified score; The subjective evaluation is conducted in the form of questionnaires to evaluate.ResultsThe modeling and image analysis of the current advanced hand-held 3D imaging scanner and our self-developed 2D fitting 3D imaging method were compared.The images (equivalent to 1, 10, and 40 × magnification) of the model points and polygons using the Cinema 4D R19 virtual camera of 50, 500, and 2000 mm showed 1,249,955 points and 2,500,122 polygons in the skull data obtained using the hand-held scanner. The 3D model data of the skull consisted of 499,914 points, while the number of polygons reached up to 60,000,000, which was about fourfold that of the hand-held 3D scanning. This model used 8 K mapping technology, and hand-held scanner 3D imaging 3D scanning modeling used a 0.13 K map based on the map data, thereby indicating that the 2D fitting 3D imaging method is delicate and real. Comparative analysis of general data of three groups of students.The comparison of test results, clinical practice assessment and teaching satisfaction of the three groups shows that the performance of hand held scanner 3D imaging group is better than that of traditional teaching group (P < 0.01), and that of 2D fitting 3D method group is significantly better than that of traditional teaching group (P < 0.01).ConclusionsThe method used in this study can achieve real reduction. Compared to hand-held scanning, this method is more cost-effective than the cost of the equipment and the results. Moreover, the post-processing is easy to master, and the autopsy can be performed easily after learning, negating the need to seek professional help. It has a wide application prospect in teaching.

Analysis and Validation of Varied Simulation Parameters in the Context of Thermoplastic Foams and Special Injection Molding Processes.

The simulation solutions of different plastic injection molding processes are as multifaceted as the field of injection molding itself. In this study, the simulation of a special injection molding process, which generates partially foamed integral components, was parameterized and performed. This partial and physical foaming is realized by a defined volume expansion of the mold cavity. Using the injection molding simulation software Moldex 3D, this so-called Pull and Foam process was digitally reconstructed and simulated. Since the Pull and Foam process is a special injection molding technique for producing foamed components, the validity of the simulation results was analyzed and evaluated. With the use of Moldex 3D, varied settings such as different bubble growth models and mesh topologies were set, parameterized, and then analyzed, to provide differentiated numerical calculation solutions. Actual manufactured components represent the experimental part of this study and are produced for reference. Different evaluation methods were used to quantify morphological quantities such as porosities, local densities, and cell distributions. These methods are based on two-dimensional and three-dimensional imaging techniques such as optical microscopy and X-ray microtomography (µ-CT). Thus, this structural characterization of the manufactured samples serves as the validation basis for the calculated results of the simulations. According to the illustrated results, the adequate selection of bubble growth models and especially mesh topologies must be considered for valid simulation of specific core-back techniques, such as the Pull and Foam injection molding process.

Electron Microscopy Studies of Soft Nanomaterials.

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. The even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Realistic Facial Three-Dimensional Reconstruction from CT Images and 2D Photographic Images for Surgical-Orthognathic Planning

Orthognathic surgery is a procedure used to correct intermaxillary discrepancies, thus promoting significant improvements in chewing and breathing. During the surgical planning stage, orthodontists often use two-dimensional imaging techniques. The assessment is based on CBCT images and dental cast models to overcome these limitations; however, the evaluation of soft tissues remains complex. The aim of the present study was to develop a co-registration method of CBCT and photo images that would result in realistic facial image reconstruction. CBCT images were three-dimensionally rendered, and the soft tissues were subsequently segmented resulting in the cranial external surface. A co-registration between the obtained surface and a frontal photo of the subject was then carried out. From this mapping, a photorealistic model capable of replicating the features of the face was generated. To assess the quality of this procedure, seven orthodontists were asked to fill in a survey on the models obtained. The survey results showed that orthodontists consider the three-dimensional model obtained to be realistic and of high quality. This process can automatically obtain a three-dimensional model from CBCT images, which in turn may enhance the predictability of surgical-orthognathic planning.

Adaptive Capon beamforming for lensless electron cyclotron emission imaging with high spatial resolution

Electron cyclotron emission (ECE) imaging diagnostics incorporating a lensless approach have been developed for measurements involving active spatial selectivity and direction-of-arrival estimation. The Capon method for adaptive-array analysis was proposed to improve the spatial resolution of the two-dimensional ECE imaging technique. Broadband noise source emissions were used to simulate the ECE to verify the practical effectiveness of the Capon method in the ECE imaging. Multiple noise source emission positions were properly estimated with a high spatial resolution using the Capon method.

A Novel Reconstruction Algorithm with High Performance for Compressed Ultrafast Imaging.

Compressed ultrafast photography (CUP) is a type of two-dimensional (2D) imaging technique to observe ultrafast processes. Intelligence reconstruction methods that influence the imaging quality are an essential part of a CUP system. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, it usually takes a lot of time to obtain acceptable reconstruction results, which limits the practical application of the CUP. In this paper, we proposed a novel reconstruction algorithm named PnP-FFDNet, which can provide a high quality and high efficiency compared to previous methods. First, we built a forward model of the CUP and three sub-optimization problems were obtained using the alternating direction multiplier method (ADMM), and the closed-form solution of the first sub-optimization problem was derived. Secondly, inspired by the PnP-ADMM framework, we used an advanced denoising algorithm based on a neural network named FFDNet to solve the second sub-optimization problem. On the real CUP data, PSNR and SSIM are improved by an average of 3 dB and 0.06, respectively, compared with traditional algorithms. Both on the benchmark dataset and on the real CUP data, the proposed method reduces the running time by an average of about 96% over state-of-the-art algorithms, and show comparable visual results, but in a much shorter running time.

2D Transformations of Energy Signals for Energy Disaggregation.

The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.

3D Imaging of the Liver Extracellular Matrix in a Mouse Model of Non-Alcoholic Steatohepatitis.

Non-alcoholic steatohepatitis (NASH) is the most common chronic liver disease in the United States, affecting more than 70 million Americans. NASH can progress to fibrosis and eventually to cirrhosis, a significant risk factor for hepatocellular carcinoma. The extracellular matrix (ECM) provides structural support and maintains liver homeostasis via matricellular signals. Liver fibrosis results from an imbalance in the dynamic ECM remodeling process and is characterized by excessive accumulation of structural elements and associated changes in glycosaminoglycans. The typical fibrosis pattern of NASH is called "chicken wire," which usually consists of zone 3 perisinusoidal/pericellular fibrosis, based on features observed by Masson's trichrome stain and Picrosirius Red stains. However, these traditional thin two-dimensional (2D) tissue slide-based imaging techniques cannot demonstrate the detailed three-dimensional (3D) ECM structural changes, limiting the understanding of the dynamic ECM remodeling in liver fibrosis. The current work optimized a fast and efficient protocol to image the native ECM structure in the liver via decellularization to address the above challenges. Mice were fed either with chow or fast-food diet for 14 weeks. Decellularization was performed after in situ portal vein perfusion, and the two-photon microscopy techniques were applied to image and analyze changes in the native ECM. The 3D images of the normal and NASH livers were reconstituted and analyzed. Performing in situ perfusion decellularization and analyzing the scaffold by two-photon microscopy provided a practical and reliable platform to visualize the dynamic ECM remodeling in the liver.

3D Imaging of the Liver Extracellular Matrix in a Mouse Model of Non-Alcoholic Steatohepatitis

Non-alcoholic steatohepatitis (NASH) is the most common chronic liver disease in the United States, affecting more than 70 million Americans. NASH can progress to fibrosis and eventually to cirrhosis, a significant risk factor for hepatocellular carcinoma. The extracellular matrix (ECM) provides structural support and maintains liver homeostasis via matricellular signals. Liver fibrosis results from an imbalance in the dynamic ECM remodeling process and is characterized by excessive accumulation of structural elements and associated changes in glycosaminoglycans. The typical fibrosis pattern of NASH is called "chicken wire," which usually consists of zone 3 perisinusoidal/pericellular fibrosis, based on features observed by Masson's trichrome stain and Picrosirius Red stains. However, these traditional thin two-dimensional (2D) tissue slide-based imaging techniques cannot demonstrate the detailed three-dimensional (3D) ECM structural changes, limiting the understanding of the dynamic ECM remodeling in liver fibrosis. The current work optimized a fast and efficient protocol to image the native ECM structure in the liver via decellularization to address the above challenges. Mice were fed either with chow or fast-food diet for 14 weeks. Decellularization was performed after in situ portal vein perfusion, and the two-photon microscopy techniques were applied to image and analyze changes in the native ECM. The 3D images of the normal and NASH livers were reconstituted and analyzed. Performing in situ perfusion decellularization and analyzing the scaffold by two-photon microscopy provided a practical and reliable platform to visualize the dynamic ECM remodeling in the liver.

Artificial intelligence for assessment of Stargardt macular atrophy.

Stargardt disease (also known as juvenile macular degeneration or Stargardt macular degeneration) is an inherited disorder of the retina, which can occur in the eyes of children and young adults. It is the most prevalent form of juvenile-onset macular dystrophy, causing progressive (and often severe) vision loss. Images with Stargardt disease are characterized by the appearance of flecks in early and intermediate stages, and the appearance of atrophy, due to cells wasting away and dying, in the advanced stage. The primary measure of late-stage Stargardt disease is the appearance of atrophy. Fundus autofluorescence is a widely available two-dimensional imaging technique, which can aid in the diagnosis of the disease. Spectral-domain optical coherence tomography, in contrast, provides three-dimensional visualization of the retinal microstructure, thereby allowing the status of the individual retinal layers. Stargardt disease may cause various levels of disruption to the photoreceptor segments as well as other outer retinal layers. In recent years, there has been an exponential growth in the number of applications utilizing artificial intelligence for help with processing such diseases, heavily fueled by the amazing successes in image recognition using deep learning. This review regarding artificial intelligence deep learning approaches for the Stargardt atrophy screening and segmentation on fundus autofluorescence images is first provided, followed by a review of the automated retinal layer segmentation with atrophic-appearing lesions and fleck features using artificial intelligence deep learning construct. The paper concludes with a perspective about using artificial intelligence to potentially find early risk factors or biomarkers that can aid in the prediction of Stargardt disease progression.