Showing papers on "Imaging technology published in 2021"

Digital postprocessing and image segmentation for objective analysis of colorimetric reactions

Abstract: 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.

Recent advances in near-infrared II imaging technology for biological detection

Abstract: Molecular imaging technology enables us to observe the physiological or pathological processes in living tissue at the molecular level to accurately diagnose diseases at an early stage. Optical imaging can be employed to achieve the dynamic monitoring of tissue and pathological processes and has promising applications in biomedicine. The traditional first near-infrared (NIR-I) window (NIR-I, range from 700 to 900 nm) imaging technique has been available for more than two decades and has been extensively utilized in clinical diagnosis, treatment and scientific research. Compared with NIR-I, the second NIR window optical imaging (NIR-II, range from 1000 to 1700 nm) technology has low autofluorescence, a high signal-to-noise ratio, a high tissue penetration depth and a large Stokes shift. Recently, this technology has attracted significant attention and has also become a heavily researched topic in biomedicine. In this study, the optical characteristics of different fluorescence nanoprobes and the latest reports regarding the application of NIR-II nanoprobes in different biological tissues will be described. Furthermore, the existing problems and future application perspectives of NIR-II optical imaging probes will also be discussed.

Firefly optimization-based segmentation technique to analyse medical images of breast cancer

Abstract: Nature-inspired algorithms emulate the mathematical and innovative techniques for non-linear and real-life problems worldwide. Imaging technology is emerging out as one of the most prominent and wi...

MRI biomarkers in neuro-oncology.

Abstract: The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Wound Size Imaging: Ready for Smart Assessment and Monitoring.

Abstract: Significance: We introduce and evaluate emerging devices and modalities for wound size imaging and also promising image processing tools for smart wound assessment and monitoring. Recent Advances: Some commercial devices are available for optical wound assessment but with limited possibilities compared to the power of multimodal imaging. With new low-cost devices and machine learning, wound assessment has become more robust and accurate. Wound size imaging not only provides area and volume but also the proportion of each tissue on the wound bed. Near-infrared and thermal spectral bands also enhance the classical visual assessment. Critical Issues: The ability to embed advanced imaging technology in portable devices such as smartphones and tablets with tissue analysis software tools will significantly improve wound care. As wound care and measurement are performed by nurses, the equipment needs to remain user-friendly, enable quick measurements, provide advanced monitoring, and be connected to the patient data management system. Future Directions: Combining several image modalities and machine learning, optical wound assessment will be smart enough to enable real wound monitoring, to provide clinicians with relevant indications to adapt the treatments and to improve healing rates and speed. Sharing the wound care histories of a number of patients on databases and through telemedicine practice could induce a better knowledge of the healing process and thus a better efficiency when the recorded clinical experience has been converted into knowledge through deep learning.

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography.

Abstract: Optical coherence tomography (OCT) is one of the most innovative and successfully translated imaging techniques with substantial clinical and economic impacts and acceptance.1,2 OCT is a non-invasive optical analog to ultrasound (US) with significantly higher resolution ( millions of A-scans/s) imaging with tissue penetration of up to 2 mm, closely matching that of conventional histopathology. The year 2021 marks not only the 30th birthday of OCT (assuming its initiation with the Science paper by Huang et al.3 in 1991) but also the 35th birthday of low-coherence interferometry and optical ranging in biological systems.4,5 In the last three decades, more than 75,000 OCT related papers have been published (about two thirds in ophthalmology) with continuous yearly increases of published articles.6 Breaking through the 1000 publications/year barrier was initiated in 2005/2006 with the introduction of spectral domain OCT (SD OCT). In 2020, the OCT-related scientific output was more than 7800 papers, resulting in nearly one paper every single hour on every single day of the year. Extrapolating this publishing performance, a saturation of yearly publication output at about 9500 can be expected around 2030. After 30 years, it is interesting and important to benchmark this performance with other medical imaging techniques:6 multiphoton microscopy (MPM) [including second harmonic generation (SHG) and third harmonic generation (THG)], developed about three decades before OCT,7,8 has about 50,000 publications so far; photoacoustic imaging (PAI), established in the 1970s,9,10 has about 15,000 papers; and confocal microscopy, developed in the 1940s,11,12 has about 145,000. Developed in the 1940s,13 US imaging has contributed to about 160,000 papers; positron emission tomography (PET), initiated in the 1970s,14,15 has about 175,000; computed tomography (CT), developed in the 1930s,16 has about 750,000; and magnetic resonance imaging (MRI), developed in the late 1940s,17 has close to 1,000,000 publications. This dominance in publications of radiology and nuclear medicine imaging technologies is also one of the reasons why medical imaging is, in general, associated with MRI, CT, PET, or US. It is important to note, though, that from a medical imaging market perspective, optical imaging technologies dominate with 66% versus 34% for radiology and nuclear medicine imaging technologies. In addition, in the United States alone, about 450,000 physicians use primarily optical imaging techniques; 60,000 use primarily radiologic imaging; and about 130,000 use both.18 In the last three decades, OCT has revolutionized ophthalmic diagnosis, therapy monitoring, and guidance. Every second, a human gets a retinal OCT scan; therefore it is the fastest adopted imaging technology in the history of ophthalmology. This is mainly due to the ease of optical accessibility of the human eye, OCT’s exquisite depth sectioning performance at the micrometer level, and a significantly better performance compared with the previous gold standard in this field, ultrasonography. Furthermore, it is also due to the fact that the human retina cannot be biopsied and finally to the continuous clinically relevant improvements of this technology, due to an exquisite ecosystem between industry and academia in terms of resolution, speed, wide-field imaging, and longer wavelength for choroidal imaging. Motion contrast-based angiography, cellular level retinal visualization, visible light OCT for oximetry and unprecedented retinal layer detection, functional and contrast enhanced extensions, and artificial intelligence (AI)-enhanced performance also contributed to this success. Most of these superb technological developments can be directly translated to the original motivation and idea of OCT: to enable optical biopsy, i.e., the in situ imaging of tissue microstructure with a resolution approaching that of histology but without the need for tissue excision and preparation, allowing for quasi-instantaneous diagnostic feedback for physicians, and thereby reducing healthcare costs. There is no doubt that outside ophthalmology, OCT faces significantly bigger challenges with extremely well performing, long-established diagnostic techniques. Hence, OCT has successfully penetrated into different medical fields outside of ophthalmology, but in the last 30 years, it has not been as successful as in ophthalmic diagnosis. Despite the unprecedented success of this imaging technique in ophthalmology so far, there are still numerous remaining challenges in this field to be addressed (e.g., 4D intrasurgical OCT, portable, handheld OCT, and OCT-based digital adaptive optics) but one of the biggest perspectives for OCT is to further push performance frontiers of all involved technologies to converge to the original motivation of OCT, which is to enable in situ optical biopsy, especially for early cancer diagnosis and for a better understanding of oncogenesis. Consequently, this perspective will focus on the following areas that will pave the way for enabling even further enhanced medical diagnosis using OCT in the future. Imaging speed is absolutely essential in medical diagnosis: on the one hand, to minimize the exam time for the patient, but foremost to enable motion artifact free, properly sampled data sets. The speed of today’s systems already supports three- and even four-dimensional imaging as well as wide fields of view and functional extensions of OCT, such as OCT angiography. In the future, different technologies will enable increased OCT imaging speed with one of the fundamental decisions being at which scanning speed single-beam raster scanning will be abandoned and scanning beam parallelization will be used. Further challenges of OCT’s unmatched axial and transverse resolution will also be discussed. Similar to combining different radiology and nuclear medicine imaging technologies in current clinical diagnosis, multimodal optical imaging not only enables the “best of both/all worlds” but also compensates for the deficits of OCT (metabolic, molecular sensitivity, penetration depth, and limited contrast). Multimodal imaging applications combining techniques complementary to OCT will more and more be transferred from significantly improved microscopy setups—acting as fast quasi-histological optical biopsies next to the operating room—to the miniaturized endoscopic level with OCT acting like a global positioning system (GPS) by prescreening the tissue at a wider field of view (FOV) with microscopic resolution. Aside from OCTA, no other functional or contrast enhancing OCT extension has accomplished comparable clinical impact in the last three decades. Some more recently developed ones that might accomplish this challenging task, including quantitative OCTA (especially in neuro-ophthalmology), optical coherence elastography (OCE), dynamic contrast OCT, oximetry using visible light OCT, optophysiology—also referred to optoretinography—and AI-enhanced OCT, will be covered in this perspective. In addition, OCT miniaturization for portable, compact, handheld OCT applications, as well as for home-OCT and self-OCT, will be discussed. Finally, industrial translation of OCT, including medical device regulatory challenges, will be reviewed.

Recent Advances in Second Near-Infrared Region (NIR-II) Fluorophores and Biomedical Applications.

Abstract: Fluorescence imaging technique, characterized by high sensitivity, non-invasiveness and no radiation hazard, has been widely applicated in the biomedical field. However, the depth of tissue penetration is limited in the traditional (400-700 nm) and NIR-I (the first near-infrared region, 700-900 nm) imaging, which urges researchers to explore novel bioimaging modalities with high imaging performance. Prominent progress in the second near-infrared region (NIR-II, 1000-1700 nm) has greatly promoted the development of biomedical imaging. The NIR-II fluorescence imaging significantly overcome the strong tissue absorption, auto-fluorescence as well as photon scattering, and can achieve centimeter depth of tissue penetration, micron-level spatial resolution, and high signal-to-noise ratio. NIR-II bioimaging has been regarded as the most promising in vivo fluorescence imaging technology. High brightness and biocompatible fluorescent probes are crucial important for NIR-II in vivo imaging. Herein, we focus on the recently developed NIR-II fluorescent cores and their applications in the field of biomedicine, especially in tumor delineation and image-guided surgery, vascular imaging, NIR-II-based photothermal therapy and photodynamic therapy, drug delivery. Besides, the challenges and potential future developments of NIR-II fluorescence imaging are further discussed. It is expected that our review will lay a foundation for clinical translation of NIR-II biological imaging, and inspire new ideas and more researches in this field.

3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation

Abstract: Millimeter-wave (MMW) 3-D imaging technology is becoming a research hotspot in the field of safety inspection, intelligent driving, etc., due to its all-day, all-weather, high-resolution and non-destruction feature. Unfortunately, due to the lack of a complete 3-D MMW radar dataset, many urgent theories and algorithms (e.g., imaging, detection, classification, clustering, filtering, and others) cannot be fully verified. To solve this problem, this paper develops an MMW 3-D imaging system and releases a high-resolution 3-D MMW radar dataset for imaging and evaluation, named as 3DRIED. The dataset contains two different types of data patterns, which are the raw echo data and the imaging results, respectively, wherein 81 high-quality raw echo data are presented mainly for near-field safety inspection. These targets cover dangerous metal objects such as knives and guns. Free environments and concealed environments are considered in experiments. Visualization results are presented with corresponding 2-D and 3-D images; the pixels of the 3-D images are 512×512×6. In particular, the presented 3DRIED is generated by the W-band MMW radar with a center frequency of 79GHz, and the theoretical 3-D resolution reaches 2.8 mm × 2.8 mm × 3.75 cm. Notably, 3DRIED has 5 advantages: (1) 3-D raw data and imaging results; (2) high-resolution; (3) different targets; (4) applicability for evaluation and analysis of different post processing. Moreover, the numerical evaluation of high-resolution images with different types of 3-D imaging algorithms, such as range migration algorithm (RMA), compressed sensing algorithm (CSA) and deep neural networks, can be used as baselines. Experimental results reveal that the dataset can be utilized to verify and evaluate the aforementioned algorithms, demonstrating the benefits of the proposed dataset.

Artificial intelligence in ophthalmopathy and ultra-wide field image: A survey

Abstract: Fundus digital photography and optical coherence tomography (OCT) are currently the primary imaging approaches for early diagnosis and treatment of eye diseases. In recent years, the significant development in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL) are new and vital technical-driven motivations impacting on the traditional diagnosis and treatment methods. At the same time, the ultra-wide field (UWF) imaging technology is getting widely accepted and prevalent by its obvious advantageous features of non-dilate pupils, express-track result and the vast pool of fundus viewing angles. As a result, numerous research have been done to explore AI in ultra-wide field fundus imaging ophthalmology for joint diagnosis and treatment. However, the current review of this method is still in least ink. We first outlines the application and impact of AI technology in ophthalmic diseases in the past ten years. With the following part exclusively summarizing the technical integration of ultra-wide field fundus images and AI technology in the past four years, which has brought innovations to clinical treatment methods for the diagnosis and treatment of ophthalmic diseases; finally, we analyzed the application and implementation of the novel technology as well as the potential limitations and challenges, to predict the possibility of the technology’s further principles role and values in clinical ophthalmology.

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation.

Abstract: Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.

Improving PCI Outcomes Using Postprocedural Physiology and Intravascular Imaging

Abstract: Although clinical outcomes after percutaneous coronary intervention (PCI) are improving, the long-term risk for target vessel failure remains concerning. Although the application of intravascular imaging and physiological indexes significantly improves outcomes, their routine use in practice remains limited. Nevertheless, merely using these modalities is not enough, and to truly improve patient outcomes, optimal intravascular dimensions with minimal vascular injury should be targeted. When assessing post-PCI results using either type of physiological or imaging technology, a broad spectrum of stent- and vessel-related anomalies can be expected. As not all of these issues warrant treatment, a profound knowledge of what to expect and how to recognize and when to treat these intraluminal problems is needed. Additionally, promising new modalities such as angiography-derived coronary physiology and hybrid imaging catheters are becoming available. The authors provide an overview of the currently available tools and techniques to define suboptimal PCI and when to apply these technologies to improve outcomes.

Image Guidance in Ablation for Hepatocellular Carcinoma: Contrast-Enhanced Ultrasound and Fusion Imaging

Abstract: The ultrasound (US) imaging technology, including contrast-enhanced US (CEUS) and fusion imaging, has experienced radical improvement, and advancement in technology thus overcoming the problem of poor conspicuous hepatocellular carcinoma (HCC). On CEUS, the presence or absence of enhancement distinguishes the viable portion from the ablative necrotic portion. Using volume data of computed tomography (CT) or magnetic resonance imaging (MRI), fusion imaging enhances the three-dimensional relationship between the liver vasculature and HCC. Therefore, CT/MR-US fusion imaging provides synchronous images of CT/MRI with real-time US, and US-US fusion imaging provides synchronous US images before and after ablation. Moreover, US-US overlay fusion can visualize the ablative margin because it focuses the tumor image onto the ablation zone. Consequently, CEUS and fusion imaging are helpful to identify HCC with little conspicuity, and with more confidence, we can perform ablation therapy. CEUS/fusion imaging guidance has improved the clinical effectiveness of ablation therapy in patients with poor conspicuous HCCs. Therefore; this manuscript reviews the status of CEUS/fusion imaging guidance in ablation therapy of poor conspicuous HCC.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring.

Abstract: Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band

Abstract: Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

Using standard X-ray images to create 3D digital bone models and patient-matched guides for aiding implant positioning and sizing in total knee arthroplasty.

Abstract: X-Atlas™ is a new imaging technology intended to advance the state of the art in patient-specific instrumentation. It uses standard AP and lateral radiographs instead of CT or MRI scans to create 3...

Dedicated breast CT: state of the art-Part I. Historical evolution and technical aspects.

Abstract: Dedicated breast CT is an emerging 3D isotropic imaging technology for breast, which overcomes the limitations of 2D compression mammography and limited angle tomosynthesis while providing some of the advantages of magnetic resonance imaging. This first installment in a 2-part review describes the evolution of dedicated breast CT beginning with a historical perspective and progressing to the present day. Moreover, it provides an overview of state-of-the-art technology. Particular emphasis is placed on technical limitations in scan protocol, radiation dose, breast coverage, patient comfort, and image artifact. Proposed methods of how to address these technical challenges are also discussed. KEY POINTS: • Advantages of breast CT include no tissue overlap, improved patient comfort, rapid acquisition, and concurrent assessment of microcalcifications and contrast enhancement. • Current clinical and prototype dedicated breast CT systems differ in acquisition modes, imaging techniques, and detector types. • There are still details to be decided regarding breast CT techniques, such as scan protocol, radiation dose, breast coverage, patient comfort, and image artifact.

3D printing innovation: New insights into upper extremity surgery planning.

Abstract: Introduction Surgical planning relies on the use of images to develop an action plan prior to the actual surgical intervention. Imaging technology improvement together with the development of specific software to treat three dimensional images has increased the accuracy and capabilities of pre-surgical planning. In addition to this, 3D printing allows us to manufacture customized surgical tools to implement and aid in the success of surgeries. Material and Methods 3D virtual planning together with 3D printing has been implemented through different approaches in 8 different upper extremity trauma cases. We describe these 8 cases (2 women and 6 men with ages ranging from 16 to 67 years), their specific challenges and management. Results We show how 3D technology changes the conception, planning and execution of surgery in 8 different cases. In addition, we describe what challenges were faced as well as the various utilities of 3D technology beyond that of anatomical model printing. Conclusions The use of 3D technology has improved and enhanced surgical planning. It allows us to view and virtually manipulate fracture fragments prior to surgery. It also enables us to develop customized surgical tools and guides that can increase the accuracy of certain procedures, and help in the management of orthopaedic and trauma lesions. We believe that the use of this technology is beneficial to both the patient and surgeon, since it reduces surgical time and complications giving a better understanding of the injury and its treatment.

Autofluorescence Image Reconstruction and Virtual Staining for In-Vivo Optical Biopsying

Abstract: Modern photonic technologies are emerging, allowing the acquisition of in-vivo endoscopic tissue imaging at a microscopic scale, with characteristics comparable to traditional histological slides, and with a label-free modality. This raises the possibility of an ‘optical biopsy’ to aid clinical decision making. This approach faces barriers for being incorporated into clinical practice, including the lack of existing images for training, unfamiliarity of clinicians with the novel image domains and the uncertainty of trusting ‘black-box’ machine learned image analysis, where the decision making remains inscrutable. In this paper, we propose a new method to transform images from novel photonics techniques (e.g. autofluorescence microscopy) into already established domains such as Hematoxilyn-Eosin (H-E) microscopy through virtual reconstruction and staining. We introduce three main innovations: 1) we propose a transformation method based on a Siamese structure that simultaneously learns the direct and inverse transformation ensuring domain back-transformation quality of the transformed data. 2) We also introduced an embedding loss term that ensures similarity not only at pixel level, but also at the image embedding description level. This drastically reduces the perception distortion trade-off problem existing in common domain transfer based on generative adversarial networks. These virtually stained images can serve as reference standard images for comparison with the already known H-E images. 3) We also incorporate an uncertainty margin concept that allows the network to measure its own confidence, and demonstrate that these reconstructed and virtually stained images can be used on previously-studied classification models of H-E images that have been computationally degraded and de-stained. The three proposed methods can be seamlessly incorporated on any existing architectures. We obtained balanced accuracies of 0.95 and negative predictive values of 1.00 over the reconstructed and virtually stained image-set on the detection of color-rectal tumoral tissue. This is of great importance as we reduce the need for extensive labeled datasets for training, which are normally not available on the early studies of a new imaging technology.

Fault detection technology of national traditional sports equipment based on optical microscope imaging technology

Abstract: The safety of national traditional sports equipment is one of the basic conditions for the development of national traditional sports. Based on this, a fault detection technology of national traditional sports equipment based on optical microscope imaging technology is proposed. The concept of confocal fluorescence is integrated into the imaging system of an optical microscope to improve the axial resolution of optical microscope and enhance its microscopic imaging effect; the microscopic images of traditional national sports equipment are collected, and the mean filtering method is used to reduce the noise of the collected microscopic images of traditional national sports equipment, and the phase images after noise reduction are processed based on the error correction algorithm, the phase image after noise reduction is fused to build a fault free standard background image and calculate the gray mean value of all pixels in the micro image. If the gray value of the pixel is higher than the confidence interval, it is determined that there is a fault. The detection results show that the proposed method can effectively detect different types of fault information, achieve the expected goal of fault detection, and the average energy consumption in the detection process is about 0.47 J/s, which is lower than the other two technologies by more than 0.4 J/s, and the fault detection performance is good.

Portable high-resolution automated 3D imaging for footwear and tire impression capture

Abstract: The forensic science community raised the need for improved evidence recognition, collection, and visualization analytical instrumentation for field and laboratory use. While the 3D optical techniques for imaging static objects have been extensively studied, there is still a major gap between current knowledge and collecting high-quality footwear and tire impression evidence. Among optical means for 3D imaging, digital fringe projection (DFP) techniques reconstruct 3D shape from phase information, achieving camera-pixel spatial resolution. This paper presents a high-resolution 3D imaging technology using DFP techniques dedicated to footwear and tire impression capture. We developed fully automated software algorithms and a graphical user interface (GUI) that allow anyone without training to operate for high-quality 3D data capture. We performed accuracy evaluations and comparisons comparing with the commercial high-end 3D scanner and carried out qualitative tests for various impressions comparing with the current practices. Overall, our technology achieves similar levels of accuracy and resolution with a high-end commercially available 3D scanner, while having the merits of being (1) more affordable; (2) much easier to operate; and (3) more robust. Compared with the current practice of casting, our technology demonstrates its superiority because it (1) is non-destructive; (2) collects more evidence detail than casts, especially when an impression is fragile; (3) requires less time and money to collect each piece of evidence; and (4) results in a digital file that can easily be shared with other examiners.

Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration.

Abstract: Age-related macular degeneration (AMD) affects nearly 200 million people and is the third leading cause of irreversible vision loss worldwide. Deep learning, a branch of artificial intelligence that can learn image recognition based on pre-existing datasets, creates an opportunity for more accurate and efficient diagnosis, classification, and treatment of AMD on both individual and population levels. Current algorithms based on fundus photography and optical coherence tomography imaging have already achieved diagnostic accuracy levels comparable to human graders. This accuracy can be further increased when deep learning algorithms are simultaneously applied to multiple diagnostic imaging modalities. Combined with advances in telemedicine and imaging technology, deep learning can enable large populations of patients to be screened than would otherwise be possible and allow ophthalmologists to focus on seeing those patients who are in need of treatment, thus reducing the number of patients with significant visual impairment from AMD.