High-definition Images Research Articles

Memory-Efficient Deformable Convolution Based Joint Denoising and Demosaicing for UHD Images

This paper introduces deformable convolution in deep learning based joint denoising and demosaicing (JDD), which yields more adaptable representation and larger receptive fields in features extraction for a superior restoration performance. However, the deformable convolution generally leads to considerable computational load and irregular memory access bottleneck, limiting its extensive deployment on edge devices. To address this issue, we develop grouping strategy and assign independent offsets to each kernel group to reduce the computation latency while keeping the accuracy. Motivated by the exploration for aggregate distribution characteristics of deformable offsets, we present the offset sharing methodology to simplify the memory access complexity of deformable convolution. As for hardware acceleration, we specially design a novel deformable matrix multiplication workflow incorporated with a deformable memory mapping unit to boost the computational throughput. The verification experiments on FPGA demonstrate that the proposed deformable convolution based JDD can restore 4K Ultra High Definition (UHD) images at 70FPS and yields significant promotion in visual effect and objective quality assessment.

The mesocolic apical fragment in complete mesocolic excision colectomies: Should it be analysed separately? A proof-of-concept study.

The aim of this work is to describe a protocol and assess the feasibility of harvesting and analysing the mesocolic apical fragment (MAF) for the presence of central lymph node (LN) metastasis and extra lymphatic free tumour cells in a random subgroup extracted from a cohort of complete mesocolic excision colectomies with central vascular ligation. Forty-seven patients diagnosed with colorectal cancer were included. A 2/2cm pyramid of tissue was cut around the central tie and sent for pathological examination. The MAF was sectioned into 16 slices. High-definition images were taken from the slices which were merged into a panoramic three-dimensional image of the MAF. The distribution of LNs in the MAF was quantified. Immunohistochemistry staining for cytokeratin 14 was used to identify isolated tumour cells and micrometastases in the extranodal tissue. No tumoural cells migrating through the apical zone, outside of the LNs, were identified. Margins of resection, mesocolic tissue and LNs were all negative in the subgroup of ultrastaged MAFs. The number of examined central LNs varied between 0 and 24, with positive MAF LNs being identified only in pN2 stages. The rate of positive apical LNs in our cohort was 4.2% (n=2). The MAF can be easily extracted from standard specimens, allowing for accurate analysis of lymphatic and extra-nodal tumour cells on the central resection margins, in central LNs and in the apical mesocolic tissue. Future research on larger cohorts is required to establish if analysing the MAF has an impact on patient staging, prognosis and management.

Multi-layer segmentation of retina OCT images via advanced U-net architecture

Optical Coherence Tomography (OCT) is a non-invasive method which can obtain high-definition images of cross section (B-scan) of the retina. By investigating the thickness of different layers of the retina in OCT images, one can diagnose ocular diseases in an early stage. Different algorithms have been proposed for retinal layer segmentation including machine learning techniques and various advanced CNN architectures, which have been developed recently. In this research, segmentation of OCT images is carried out for 9 boundaries, equivalent to segmenting eight retinal layers. We investigate different U-net like structures which can be combined with VGG and ResNet architectures to train models using labelled examples, and accuracy for the predicted retinal layers would be compared. In reducing the complexity of networks, a method is proposed based on the concept of domain decomposition when training a large volume of data on a cloud platform.

Testing a new automated macrocharcoal detection method applied to a transect of lacustrine sediment cores in eastern Canada

Over the past decades, the abundance and area of macrocharcoal (i.e. ≥ 150 μm in diameter) fragments from sedimentary sequences have been quantified using visual or semi-automated methods to reconstruct fire histories. However, the lack of uniformity between counting methods used in each study could introduce methodological biases influencing fire frequency reconstructions, and therefore impact their interpretation and limit their comparisons. To overcome this issue, we propose here a new automated method to quantify the number of macrocharcoal fragments and measure their areas from high-definition image capture, based on the analysis of colorimetric parameters. We tested the efficiency of our method and reconstructed charcoal influx over the last 8000 years by comparing visual and automatic counting methods along a north-south transect from eastern Canada, estimating number and size, and the associated local and regional fire frequencies. Results show that our automated method is efficient in detecting charcoal particles, except for highly minerogenic samples, and suggest that the traditional visual inspection tends to overestimate the size of macrocharcoal fragments. Local fire frequencies varied greatly depending on the macrocharcoal detection method used. At the regional scale they seem closer, and our automated method reproduces similar trends to published studies in our study area. However, it does represent a methodological advancement, particularly for recent centuries, that should be considered for future paleoecological studies.

S2334 Incidentally Diagnosed Triple Cancers Within 2 Years: A Novel Case of Rare Metachronous Cancers, Renal Cell Carcinoma, Cholangiocarcinoma, and Squamous Cell Carcinoma of the Gastroesophageal Junction

Introduction: The gastrointestinal (GI) tract is involved in many familial cancer syndromes usually involving the colon or stomach. We present a patient with 3 rare cancers that were detected within a span of 2 years in a single individual: clear cell renal cell carcinoma (ccRCC), cholangiocarcinoma (CCA), and esophageal squamous cell carcinoma (SCC); a combination not previously described in the literature Case Description/Methods: A 50-year-old male with a history of ccRCC, GERD, and CCA was admitted for evaluation of chronic abdominal pain during chemotherapy for CCA. Regarding his cancer history, ccRCC was incidentally diagnosed 18 months prior on a CT scan for diverticulitis. He underwent a left partial nephrectomy. Surveillance CT imaging 6 months after surgery revealed new incidental liver lesions. Biopsies confirmed metastatic CCA and he started chemotherapy. He noted a 15-year history of episodic abdominal pain and vomiting with no dysphagia or weight loss. Upper endoscopy 16 months prior was normal. He was a former smoker, drank alcohol a few times per month, used marijuana daily, and used cocaine rarely. His family history was significant for multiple cancers at a young age on his maternal side of unknown type. Physical exam showed diffuse abdominal tenderness, worse in the epigastrium without guarding. Labs showed mild anemia, normal BMP/LFTs, and negative H. pylori stool antigen. Upper endoscopy found nodularity adjacent to the esophagogastric junction (EGJ) with abnormal pit pattern under narrow band imaging (Figure). Biopsies confirmed invasive SCC. Staging endoscopic ultrasound showed T3N3 EGJ SCC. He was referred to oncology to discuss treatment options. Genetic testing was positive for BAP1 tumor predisposition syndrome (BAP1-TPDS) Discussion: A review of published literature did not yield a previous description of this combination of cancers in a single individual. BAP1-TPDS is an uncommon autosomal dominant syndrome caused by germline mutation in a tumor suppressor gene. Patients typically present with melanocytic tumors or malignant mesotheliomas but are at risk for RCC, hepatocellular carcinoma, meningioma and CCA. Literature has not revealed esophageal SCC to be associated. In addition to the incidental nature and timing of diagnosis of all 3 of these cancers within a short period of 2 years, the EGJ involvement is unusual. This case suggests atypical EGJ-SCC may be considered as part of the BAP1-TPDS tumor spectrum and warrant additional screening for at-risk individuals.Figure 1.: Left panel showing abnormal nodular mucosa adjacent to the gastro-esophageal junction under high-definition white light imaging. Right panel showing the same area under narrow band imaging.

Task Allocation and Traffic Route Optimization in Hybrid Fire-fighting Unmanned Aerial Vehicle Network

With the increase of extreme weather conditions in the world, the probability of forest fires is increasing. How the forest fire management decision-making system can monitor and control the fire quickly and effectively is the key of forest fire fighting work. This paper uses SSA drones carrying high-definition and thermal imaging cameras and telemetry sensors in conjunction, as well as Repeater drones used to greatly expand the frontline low-power radio range, to support fire management decision-making systems. At the same time, explore a drone cooperation plan to deal with different fire terrains and different scales of fire conditions. The aim of this paper is to improve the existing fire management decision system in order to quickly respond to the emergency fire. Research object for the Australian state of Victoria on October 1, 2019 to January 7, 2020 wildfires, explore SSA drones and Repeater drones in the application of the forest fire, ensure that fire management decision-making system to provide the optimal number deployment scheme of fire task quickly and efficiently, and achieve the maximum efficiency and economic optimal compatibility.

Wheat lodging extraction using Improved_Unet network.

The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging.

RIFIS: A Novel Rice Field Sidewalk Detection Dataset for Walk-Behind Hand Tractor

Rice field sidewalk (RIFIS) identification plays a crucial role in enhancing the performance of agricultural computer applications, especially for rice farming, by dividing the image into areas of rice fields to be ploughed and the areas outside of rice fields. This division isolates the desired area and reduces computational costs for processing RIFIS detection in the automation of ploughing fields using hand tractors. Testing and evaluating the performance of the RIFIS detection method requires a collection of image data that includes various features of the rice field environment. However, the available agricultural image datasets focus only on rice plants and their diseases; a dataset that explicitly provides RIFIS imagery has not been found. This study presents an RIFIS image dataset that addresses this deficiency by including specific linear characteristics. In Bali, Indonesia, two geographically separated rice fields were selected. The initial data collected were from several videos, which were then converted into image sequences. Manual RIFIS annotations were applied to the image. This research produced a dataset consisting of 970 high-definition RGB images (1920 × 1080 pixels) and corresponding annotations. This dataset has a combination of 19 different features. By utilizing our dataset for detection, it can be applied not only for the time of rice planting but also for the time of rice harvest, and our dataset can be used for a variety of applications throughout the entire year.

Damage detection of large concrete structure via dual imaging system and contrastive embedding

Regular surface damage detection of large concrete structures is one of the important measures to ensure their stableness and reliability. Recent advancements in computer vision and deep learning have been increasingly applied to the high-precision detection of concrete surface damage. However, most damage monitoring and localization methods are based on high-resolution images taken from close range, and the images usually contain tiny areas of actual structures. This paper proposes a contrastive embedding model to detect and localize damage on a wide range of concrete images. To achieve high-definition imaging of damage to the surface of large structures, a multifocus, and high-resolution imaging system is designed and employed. Furthermore, a concrete surface damage dataset containing 1006 detection areas and 45,375 images with seven types is constructed by manual annotation based on the obtained multiscale and multiresolution images. In addition, a contrastive embedding model based on a deep neural network is then modified, trained, and tested using the constructed dataset. To the best of our knowledge, this paper is the first to jointly use contrastive embedding to deal with damage monitoring and localization. Moreover, an evaluation framework based on sliding window iteration and nonmaximum suppression is proposed to verify the robustness and accuracy of the proposed contrastive embedding model. Experiments show that the best model achieves the classification accuracy of up to 94.84% and localization of 97.57%.

Inflammatory infiltration of the cardiac conduction system in acute lymphocytic myocarditis.

A 53-year-old man with prior history of bone marrow transplant after acute myeloblastic leukaemia was admitted in the acute cardiac care unit due to suspected acute myocarditis. At admission, his electrocardiogram (ECG) showed right bundle branch block and left anterior fascicular block (Panel A). Previously, he had been assessed regularly in a dedicated cardio-oncology clinic, with normal ECG and transthoracic echocardiography (see Supplementary material online, Videos S1 and S2). Despite high-dose pulses of methylprednisolone, he developed both complete atrio-ventricular (AV) block and progressive left ventricular systolic dysfunction (see Supplementary material online, Videos S3 and S4), leading to cardiogenic shock requiring temporary transvenous pacing and mechanical circulatory support with VA-ECMO. However, he died on day 9 due to multiorgan failure. Gross examination of the heart (Panel B) was normal, but histopathology and immunohistochemistry showed significant infiltration of the ventricular myocardium (Panel C), AV node (Panel D) left anterior fascicle (Panel E), and right bundle branch (Panel F) by mononuclear CD3 + T cells, consistent with the diagnosis of lymphocytic myocarditis. High-definition images S1-S8 are included in the Supplementary material online.

Contactless medical equipment AI big data risk control and quasi thinking iterative planning

Research Background, the intelligent polymorphic system of heavy core clustering fitting iterative programming is constructed by using the edge lens of dual core heavy core. The tracking system of heavy core TANH equilibrium array is used to obtain the abnormal data range. The energy regular fluctuation of the edge lens with dual core and heavy core is used to obtain high-definition images. And build the complexity dependent parameter group from low-end equipment to high-end equipment. Heavy core clustering of hierarchical fuzzy clustering system based on differential incremental balance theory is applied to Contactless medical equipment AI big data risk control and quasi thinking iterative planning. At the same time, the mathematical model risk control is performed by fitting the TANH balance of the local nonlinear random regular micro-vibration diffusion curve. The CT/MR original data is subjected to hierarchical cross domain overlapping grid screening with the structure of fitting weakly nonlinear curve, which can capture the heavy core cluster analysis of the core layer of big data anomalies [1:10]. Successfully control the parameter group of CT/MR machine internal data, big data AI Mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. The same time, it can prevent the dimension disaster caused by the construction of high-dimensional big data due to the partial loss of original data, and form a stable and predictable maintenance of CT/MR. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT/MR in the entire life cycle. It provides help for predictive maintenance of early pre-inspection and orderly maintenance of the medical system, and developed standardized model software of automated unsupervised learning for medical big equipment big data AI Mathematical model risk control. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are performed in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time (Yanwei et al. in J Complex 2017:1–9, 2017. https://doi.org/10.1155/2017/3437854). Finally, after adding micro-vibration quasi thinking iterative planning for the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and analyze images. This kind of AI big data risk control improves the intelligent management ability of medical institutions. Cross platform embedded web system for predictable maintenance of AI big data is established (Qi et al. in J IEEE Trans Ind Inf 99:1, 2020. https://doi.org/10.1109/tii.2020.3012157).

Nondestructive evaluation of select bridges in Iowa, USA using high-speed and ground-coupled robotic NDE platforms

This paper intends to provide a detailed assessment of the condition of two select bridge decks in the state of Iowa, USA. The field-data collection was obtained by a novel data collection system called “Infratek insight” which consists of the High Definition (HD) and High Speed (HS) platforms. The data collection took place in two phases. Phase one (HS) used air coupled NDE sensors at traffic speed while phase two (HD) utilized ground coupled NDE sensors using robotic deployment, autonomous navigation and driving systems to eliminate location variability. The data acquisition during both phases was subject to real-time data quality control by a custom data acquisition software that was developed by Infratek. Manual chain dragging was also performed at select areas to inspect the bridge deck condition and to validate data obtained by the insight system. A suite of NDE technologies were utilized on the High-Speed (HS) and High Definition (HD) platforms including Impact Echo (IE), Ultrasonic Surface Waves (USW), Electrical Resistivity (ER), Ground-Coupled GPR, Air-Coupled GPR, Surface Laser Profilers, 360-Degree Visual Imaging, Infrared thermography (IR), High-Definition Line Scan Imaging and Automated Crack Mapping with Artificial Intelligence Engines, LiDAR and Automated High-Speed Sounding (Chain Drag). ER and GPR are used to assess the corrosion potential throughout the bridge deck. GPR is also used to determine cover depth. IE, Chain Drag and IR are the primary delamination evaluation tools, and USW enables evaluation of concrete quality and degradation. Results indicated a high degree of correlation between the HD and HS platforms and also between individual sensors. The results and maps produced by the HS and HD platforms were utilized in quantifying repairs and providing the asset owner with data-driven recommendations on future rehabilitation actions based on best practices and owner specific criteria.

Wide-Area Three-Dimensional Imaging of Mesophotic Coral Reefs Using a Low-Cost AUV

Abstract Surveys and monitoring are essential to study the biology and ecology of coral reefs in order to understand the reasons behind reef demise and recovery. However, mesophotic coral ecosystems (MCEs) remain largely unknown compared to their shallow counterparts because it is difficult to observe the seafloor below 30 m depths. Here, we propose a seafloor observation method using a low-cost autonomous underwater vehicle (AUV). This method was developed to allow many people to easily perform underwater observations of MCEs, and is also applicable to general seafloor surveys, such as underwater artificial structures, hydrothermal vent fields, etc. The method possesses three important attributes to achieve effective surveys. First, it can be applied in highly rugged terrains and enables the AUV to track omnidirectional surfaces at high speed (Max. ~1 m/s). Second, it can produce a reliable three-dimensional (3D) image of the seafloor based on a depth sensor. Third, it can be used to estimate the flow velocity field. The proposed method was tested by using the AUV HATTORI-2, which is a lightweight, one-man portable AUV equipped with commercial off-the-shelf sensors. Our study area is the Sekisei Lagoon in the South Ryukyus, which represents the largest coral reef complex in Japan. The method enabled us not only to obtain a wide and high-definition 3D image of mesophotic coral reefs that captures detailed characteristics of marine habitats but also to acquire environmental data, such as flow velocity field and seawater temperature, spatially and temporally aligned with the 3D image.

Assessment of Outcomes From 1-Year Surveillance After Detection of Early Gastric Cancer Among Patients at High Risk in Japan

Single endoscopic examination often misses early gastric cancer (GC), even when both high-definition white light imaging and narrow-band imaging are used. It is unknown whether new GC can be detected approximately 1 year after intensive index endoscopic examination. To examine whether new GC can be detected approximately 1 year after intensive index endoscopic examination using both white light and narrow-band imaging. This case-control study was a preplanned secondary analysis of a randomized clinical trial involving 4523 patients with a high risk of GC who were enrolled between October 1, 2014, and September 22, 2017. Data were analyzed from December 26, 2019, to April 21, 2021. Participants in the clinical trial received index endoscopy to detect early GC via 2 examinations of the entire stomach using white light and narrow-band imaging. The duration of follow-up was 15 months. The secondary analysis included 107 patients with newly detected GC (case group) and 107 matched patients without newly detected GC (control group) within 15 months after index endoscopy. Surveillance endoscopy was scheduled between 9 and 15 months after index endoscopy. If new lesions suspected of being early GC were detected during surveillance endoscopy, biopsies were obtained to confirm the presence of cancer. The primary end point was the rate of new GC detected within 15 months after index endoscopy. The main secondary end point was identification of risk factors associated with new GC detected within 15 months after index endoscopy. Among 4523 patients (mean [SD] age, 70.6 [7.5] years; 3527 men [78.0%]; all of Japanese ethnicity) enrolled in the clinical trial, 4472 received index endoscopy; the rate of early GC detected on index endoscopy was 3.0% (133 patients). Surveillance endoscopy was performed in 4146 of 4472 patients (92.7%) who received an index endoscopy; the rate of new GC detected within 15 months after index endoscopy was 2.6% (107 patients). Among 133 patients for whom early GC was detected during index endoscopy, 110 patients (82.7%) received surveillance endoscopy within 15 months after index endoscopy; the rate of newly detected GC was 10.9% (12 patients). For the secondary analysis of risk factors associated with newly detected GC, characteristics were well balanced between the 107 patients included in the case group vs the 107 patients included in the matched control group (mean [SD] age, 71.7 [7.2] years vs 71.8 [7.0] years; 94 men [87.9%] in each group; 82 patients [76.6%] vs 87 patients [81.3%] with a history of gastric neoplasm). Multivariate analysis revealed that the presence of open-type atrophic gastritis (odds ratio, 6.00; 95% CI, 2.25-16.01; P < .001) and early GC detection by index endoscopy (odds ratio, 4.67; 95% CI, 1.08-20.21; P = .04) were independent risk factors associated with new GC detection. In this study, the rate of new GC detected by surveillance endoscopy approximately 1 year after index endoscopy was similar to that of early GC detected by index endoscopy. These findings suggest that 1-year surveillance is warranted for patients at high risk of GC.

Long-term shoreline evolution (Late-Holocene – 2018) of the Channel 15 coast (Samborombón bay, NE – Buenos Aires province, Argentina)

Here we present an investigation with the aim of reconstructing the shoreline evolution and geomorphological changes in the central zone of Samborombón bay (Channel 15, Buenos Aires province, Argentina) during the period 2700 cal yr BP to recent times. Palynological, geomorphological and historical imagery and proxy-data analyses were combined to reconstruct the shoreline evolution and paleo-environmental changes. Palynological material (pollen and non-pollen palynomorphs -NPP-) was recovered from a continuous 117 cm long sedimentary profile extracted from an outcrop at the northern margin of Channel 15. Relict landforms, including paleo- shorelines, were reconstructed by applying enhancing filters to a Spot 6 image from 2015. Historical shorelines were identified in high-definition images (aerial and satellite imagery) between 1968 and 2018, but also by using the marsh vegetation edge as a shoreline proxy. Shoreline change was quantified using the Net Shoreline Movement and Weighted Linear Regression algorithms in ArcMap 10.1. Results based on the palynological analysis for the period 2700–2740 BP, show the passage from subtidal-intertidal to supratidal environment with influence of brackish to fresh-waters, where paleo-shorelines imprint was recognized developing over a tidal plain that was grading to marshes. Results based on aerial and satellite imagery for the period 1968–2018 show shoreline retrocession only immediately at the north of the channel mouth, while the rest of the coast prograded toward the estuary. These processes indicate that new marshes – and, therefore, wetlands – are constantly developing at the edge of Samborombón bay.

New method for real-time visualization and quantitative characterization of early colorectal cancer in endoscopy: a pilot study.

Background and study aims Endoscopic optical diagnosis is crucial to the therapeutic strategy for early gastrointestinal cancer. It accurately (> 85 %) predicts pT category based on microsurface (SP) and vascular patterns (VP). However, interobserver variability is a major problem. We have visualized and digitalized the graded irregularities based on bioinformatically enhanced quantitative endoscopic image analysis (BEE) of high-definition white-light images. Methods In a pilot study of 26 large colorectal lesions (LCLs, mean diameter 39 mm), we retrospectively compared BEE variables with corresponding histopathology of the resected LCLs. Results We included 10 adenomas with low-grade intraepithelial neoplasia (LGIN), nine with high-grade intraepithelial neoplasia (HGIN) and early adenocarcinoma (EAC), and seven deeply submucosal invasive carcinomas. Quantified density (d) and nonuniformity (C U ) of vascular and surface structures correlated with histology (r s d VP: –0.77, r s C U VP: 0.13, r s d SP: –0.76, and r s C U SP: 0.45, respectively). A computed BEE score showed a sensitivity and specificity of 90 % and 100 % in the group with LGINs, 89 % and 41 % in the group with HGINs and EACs, and 100 % and 95 % in the group with deeply invasive carcinoma, respectively. Conclusions In this pilot study, BEE showed promise as a tool for endoscopic characterization of LCLs during routine endoscopy. Prospective clinical studies are needed.

3D 4K exoscope in snore surgery simulation: pre-clinical experience.

The application of 3D exoscopic technology is spreading worldwide, in several surgical scenarios. In this study, we present the first-time use of the exoscopic system (VITOM® and Versacrane™) in a cadaver simulation of transoral Snore Surgery. All participants (n = 14) were asked to perform 2 exercises that simulate tasks required in Snore Surgery, they were then administered a questionnaire assessing their evaluation of the applied exoscopic technology. Participants were divided into groups according to age and experience. Mean zooming and focusing time was higher in young surgeons than in seniors, and similar results were obtained for mean procedural times. The responses to the questionnaire showed that in the vast majority (86%), the exoscopic technology was well rated. The exoscope can be considered a useful tool, thanks to its magnifying power and high-definition images, as well as for its indirect ability to enhance staff involvement in the procedure and for educational purposes.

Towards a revision of the bamboo corals (Octocorallia): Part 5, new genera and species of Keratoisididae from the Tasmanian deep sea

Four new genera and six new species of deep-sea bamboo corals (Family: Keratoisididae) from the seamounts off the eastern and southern coast of Tasmania are described and illustrated. In situ high-definition images from remotely operated vehicles, compound microscopy, and Scanning Electron Microscopy (SEM) of 29 specimens collected at depths of 729 to 3950 m were used to provide data on distinguishing morphological characters. The species described include two new species in the genus Keratoisis, two species in the newly described genus Adinisis, and one species each in the new genera Tanyostea and Dokidisis. The species Keratoisis magnifica and Lepidisis solitaria are re-assigned to the newly described genus Onkoisis due to the shape and design of the polyp arrangement and sclerites, which differ considerably from that of the type species of Keratoisis.

Image-Enhanced Endoscopy Surveillance of Colon and Pouch Dysplasia in IBD.

Patients with longstanding ulcerative colitis and Crohn's colitis are at risk for developing colorectal cancer and need regular endoscopic surveillance to detect and remove precursor lesions. To do so, different technologies are available. The sources are observational and controlled studies, meta-analysis, and expert consensus articles available on PubMed. The selected materials include articles reporting outcomes of and recommendations on endoscopic surveillance and resection of dysplasia in the gastrointestinal tract, including the ileoanal pouch and the anal transition zone, in patients with inflammatory bowel disease. Incidence and detection rate of dysplasia and cancer with different endoscopic techniques in patients with inflammatory bowel disease. Risk of cancer is proportional to the duration and extent of the disease, and surveillance interval should be tailored on the individual risk in a range of 1 to 5 years. High-definition imaging and virtual chromoendoscopy have improved the detection of dysplasia and are now comparable with conventional dye spray chromoendoscopy. After restorative proctocolectomy with ileoanal pouch, the risk of cancer is modest, but its high mortality warrants endoscopic surveillance. The evidence to guide pouch surveillance is limited, and recently, the first expert consensus provided a framework of recommendations, which include an initial assessment 1 year after surgery and follow-up depending on individual risk factors. The limitation includes scarcity of data on ileoanal pouch surveillance. Virtual chromoendoscopy and high-definition imaging have improved endoscopic surveillance, and more progress is expected with the implementation of artificial intelligence systems.

Grating-based in-line geometric-phase-shifting incoherent digital holographic system toward 3D videography.

Incoherent digital holography (IDH) with a sequential phase-shifting method enables high-definition 3D imaging under incoherent lights. However, sequential recording of multiple holograms renders IDH impractical for 3D videography. In this study, we propose grating-based in-line geometric-phase-shifting IDH. Our method divides orthogonal circularly polarized lights into four copies with a fabricated phase grating and subsequently creates self-interference holograms with geometric phases introduced by a segmented linear polarizer. This enables single-shot recording of holograms without the need for a specially designed image sensor, such as a polarization-sensitive sensor. Moreover, the achievable spatial resolution is higher than that of off-axis methods. As a proof-of-principle experiment, we demonstrated snapshot and video recording of 3D reflective objects using our IDH method. The results confirmed the feasibility of the proposed method.