Zoom Level Research Articles

Neurosurgical applications of the exoscope: from in vitro studies to real-life surgical use in selective dorsal rhizotomy

BackgroundThe microscope has been the gold standard in neurosurgical practice due to its ability to magnify anatomical structures. However, it has limitations, including restricted visual fields and ergonomic challenges that can lead to surgeon fatigue and musculoskeletal issues. The exoscope is an emerging technology that may address these limitations by offering comparable magnification with improved ergonomics. MethodsThis study compares the traditional microscope (KINEVO 900) with a 3D digital exoscope (Aeos Digital Microscope) in visual field width, image sharpness, and ergonomic impact. Visual field assessments were conducted using millimeter paper at a fixed distance, while image sharpness was evaluated using graph paper with pins at different depths. Ergonomic evaluation involved simulating surgical positions using a spine anatomical model. The practical applicability was tested during Selective Dorsal Rhizotomy (SDR) procedures, comparing the surgeon's experience with both devices over 20 consecutive cases. ResultsThe exoscope provided a larger visual field (81.18 cm2) compared to the microscope's (54.10 cm2). Image sharpness was similar for both devices across various depths and zoom levels. Ergonomically, the exoscope allowed the surgeon to maintain a neutral posture while visualizing extreme angles, unlike the microscope, which required significant upper body movement. In SDR procedures, the exoscope improved surgeon comfort and interaction with the operating team, despite an initial learning curve. ConclusionsThe exoscope presents notable advantages in terms of visual field and ergonomics. The exoscope’s ability to facilitate better posture and team communication without compromising image quality makes it an addition to neurosurgical practice, as in SDR.

LOD4Culture: Easy exploration of cultural heritage linked open data

LOD4Culture is a web application that exploits Cultural Heritage Linked Open Data for tourism and education purposes. Since target users are not fluid on Semantic Web technologies, the user interface is designed to hide the intricacies of RDF or SPARQL. An interactive map is provided for exploring world-wide Cultural Heritage sites that can be filtered by type and that uses cluster markers to adapt the view to different zoom levels. LOD4Culture also includes a Cultural Heritage entity browser that builds comprehensive visualizations of sites, artists, and artworks. All data exchanges are facilitated through the use of a generator of REST APIs over Linked Open Data that translates API calls into SPARQL queries across multiple sources, including Wikidata and DBpedia. Since March 2022, more than 1.7K users have employed LOD4Culture. The application has been mentioned many times in social media and has been featured in the DBpedia Newsletter, in the list of Wikidata tools for visualizing data, and in the open data applications list of datos.gob.es.

Adaptive infrared patterns for microscopic surface reconstructions.

Multi-zoom microscopic surface reconstructions of operating sites, especially in ENT surgeries, would allow multimodal image fusion for determining the amount of resected tissue, for recognizing critical structures, and novel tools for intraoperative quality assurance. State-of-the-art three-dimensional model creation of the surgical scene is challenged by the surgical environment, illumination, and the homogeneous structures of skin, muscle, bones, etc., that lack invariant features for stereo reconstruction. An adaptive near-infrared pattern projector illuminates the surgical scene with optimized patterns to yield accurate dense multi-zoom stereoscopic surface reconstructions. The approach does not impact the clinical workflow. The new method is compared to state-of-the-art approaches and is validated by determining its reconstruction errors relative to a high-resolution 3D-reconstruction of CT data. 200 surface reconstructions were generated for 5 zoom levels with 10 reconstructions for each object illumination method (standard operating room light, microscope light, random pattern and adaptive NIR pattern). For the adaptive pattern, the surface reconstruction errors ranged from 0.5 to 0.7 mm, as compared to 1-1.9 mm for the other approaches. The local reconstruction differences are visualized in heat maps. Adaptive near-infrared (NIR) pattern projection in microscopic surgery allows dense and accurate microscopic surface reconstructions for variable zoom levels of small and homogeneous surfaces. This could potentially aid in microscopic interventions at the lateral skull base and potentially open up new possibilities for combining quantitative intraoperative surface reconstructions with preoperative radiologic imagery.

Visual place recognition for aerial imagery: A survey

Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository — https://github.com/prime-slam/aero-vloc.

Chirality Detection in Scanning Tunneling Microscopy Data Using Artificial Intelligence.

Enantiospecific effects play an uprising role in chemistry and technical applications. Chiral molecular networks formed by self-assembly processes at surfaces can be imaged by scanning probe microscopy (SPM). Low contrast and high noise in the topography map often interfere with the automatic image analysis using classical methods. The long SPM image acquisition times restrain Artificial Intelligence-based methods requiring large training sets, leaving only tedious manual work, inducing human-dependent errors and biased labeling. By generating realistic looking synthetic images, the acquisition of real datasets is avoided. Two state-of-the-art object detection architectures are trained to localize and classify chiral unit-cells in a regular molecular chiral network formed by self-assembly of linear molecular bricks. The comparison of different architectures and datasets demonstrates that the training on purely synthetic data outperforms models trained using augmented datasets. A Faster R-CNN model trained solely on synthetic data achieved an excellent mean average precision of 99% on real data. Hence this approach and the transfer to real data show high success, also highlighting the high robustness against experimental noise and different zoom levels across the full experimentally reasonable parameter range. The generalizability of this idea is demonstrated by achieving equally high performance on a different structure,too.

Effects of temporal features and product image zooming in online time scarcity deals: A construal fit account

Time scarcity deals, which offer time-limited discounts, are prevalent in online shopping. These deals have two temporal aspects: deal time and lead time. Deal time, the remaining duration before the discount expires, signals scarcity and enhances the deal's appeal. In contrast, lead time, the delay between purchase and receipt/consumption, postpones gratification and diminishes the deal's attractiveness. This study proposes that as deal time and lead time fluctuate, they progressively bring consumers psychologically closer to the act of purchase/consumption. Drawing on construal level theory, we contend that firms can use image zooming as a digital tool to generate product images at various zoom levels and align with the psychological distances induced by deal time or lead time throughout the deal's availability. This strategy amplifies the positive effects of deal time and mitigates the negative impacts of lead time. Our research model is validated by data from three experiments. We discovered that (1) product image zooming affects consumers’ psychological proximity to an online deal; (2) when a deal with a short (long) deal/lead time is paired with a zoom-in (zoom-out) product image, deal evaluation and purchase intention increase; and (3) these effects are mediated by consumers’ “feeling right” experience during deal evaluation, which arises from a match between the construal levels prompted by the temporal feature and the product image. Theoretical contributions and managerial implications are discussed.

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

Background:Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model’s viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. Methods:We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. Results:The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. Conclusions:The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

FCCS-Net: Breast cancer classification using Multi-Level fully Convolutional-Channel and spatial attention-based transfer learning approach

Breast cancer ranks among one of the most lethal cancer varieties among the many types that exist. Timely detection is paramount, as late diagnosis can exacerbate its severity. Computer-aided detection systems can complement the clinician in early decision-making. Therefore, in this study, a multi-level, complete convolution-driven attention-based transfer learning approach named ‘FCCS-Net’ has been proposed, for breast cancer classification. In contrast to the shared multi-layer perceptron (MLP)-based attention mechanism, the proposed approach employs a fully convolutional attention mechanism to focus the important cellular features in inter-channel and intra-channel feature space. This proposed attention is applied across multiple levels of a pre-trained ResNet18 model, supplemented with additional residual connections. The performance of the proposed FCCS-Net is tested on publicly available datasets such as ‘BreakHis’,‘IDC’ and ‘BACH’, containing breast cancer histopathology images. On the BreakHis dataset, the proposed method achieves accuracy rates of 99.25%, 98.32%, 99.50%, and 96.98% at 40X, 100X, 200X, and 400X optical zoom levels, respectively. In the case of the IDC dataset, a classification accuracy of 90.58% is attained at 40X magnifications, whereas with BACH dataset 91.25% average classification accuracy has been obtained. These findings establish the robustness and efficacy of the FCCS-Net in detecting breast cancer through histopathology images. The area focused by each attention layer has also been visually explained. The integration of multi-level, fully convolutional attention with supplementary residual connections holds the potential to advance breast cancer detection methodologies. The relevant PyTorch code for implementing the FCCS-Net model can be accessed at https://github.com/maurya123ritesh47/FCCS-Net.

Multi-Dataset Convolutional Neural Network Model for Glaucoma Prediction in OCT Fundus Scans

Glaucoma, a major factor in permanent blindness across the globe, necessitates accurate and efficient diagnostic methods. This article presents a comprehensive approach to glaucoma prediction by combining three diverse datasets: ORIGA, ACRIMA, and REFUGE. A novel multi-Datasets CNN (MD-CNN) architecture is proposed, specifically tailored to effectively handle combined data comprising diverse image characteristics across multiple datasets. This innovative approach demonstrates remarkable robustness in accommodating variations in image attributes, including lighting, zoom levels, and other disparate features, thus showcasing its potency in addressing glaucoma prediction across different datasets. The approach demonstrates improved accuracy (96.88%), sensitivity (94.34%), specificity (97.20%), precision (94.34%), and AUC (99.02%) compared to individual dataset-based models, addressing challenges in glaucoma detection. This research showcases the potential of combining diverse datasets for more effective CNN-based glaucoma detection.

The Hidden Value of Bridging Boundaries in Service Design: Fostering interconnectedness for the betterment of humanity and the planet

Service designers often oscillate between different spaces and zoom levels. This puts them in a unique position to bridge various institutional boundaries. Facilitating, nurturing and sustaining the relationships at these boundaries is not only emotionally and mentally cumbersome, but the impact of this work is also difficult to measure.

More is less - Adding zoom levels in multi-scale maps to reduce the need for zooming interactions

When you zoom in or out of current multi-scale cartographic applications, it is common to feel lost and disoriented for a few seconds because dimensions and map symbols have changed. To make the exploration of these multi-scale maps more fluid, one option is to design maps where the transformations due to scale change are more progressive. This paper proposes to use cartographic generalization techniques to design these multi-scale maps with additional intermediate scales to improve progressiveness. These more progressive maps are tested in a user study with a task requiring multiple zooms in and out. The users perform better with the progressive maps, and in particular, the total quantity of required zooming is reduced compared to maps without additional intermediate scales. However, the survey is not fully conclusive on task performance due to the complexity of such a survey with real maps. This difficulty in assessing how well progressive map generalisation reduces disorientation is discussed and guidelines are proposed to design further studies.

An efficient visual exploration approach of geospatial vector big data on the web map

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD.

Efficient Hybrid Zoom Using Camera Fusion on Mobile Phones

DSLR cameras can achieve multiple zoom levels via shifting lens distances or swapping lens types. However, these techniques are not possible on smart-phone devices due to space constraints. Most smartphone manufacturers adopt a hybrid zoom system: commonly a Wide ( W ) camera at a low zoom level and a Telephoto ( T ) camera at a high zoom level. To simulate zoom levels between W and T , these systems crop and digitally upsample images from W , leading to significant detail loss. In this paper, we propose an efficient system for hybrid zoom super-resolution on mobile devices, which captures a synchronous pair of W and T shots and leverages machine learning models to align and transfer details from T to W. We further develop an adaptive blending method that accounts for depth-of-field mismatches, scene occlusion, flow uncertainty, and alignment errors. To minimize the domain gap, we design a dual-phone camera rig to capture real-world inputs and ground-truths for supervised training. Our method generates a 12-megapixel image in 500ms on a mobile platform and compares favorably against state-of-the-art methods under extensive evaluation on real-world scenarios.

Development of Automated Risk Stratification for Sporadic Odontogenic Keratocyst Whole Slide Images with an Attention-Based Image Sequence Analyzer

(1) Background: The categorization of recurrent and non-recurrent odontogenic keratocyst is complex and challenging for both clinicians and pathologists. What sets this cyst apart is its aggressive nature and high likelihood of recurrence. Despite identifying various predictive clinical/radiological/histopathological parameters, clinicians still face difficulties in therapeutic management due to its inherent aggressive nature. This research aims to build a pipeline system that accurately detects recurring and non-recurring OKC. (2) Objective: To automate the risk stratification of OKCs as recurring or non-recurring based on whole slide images (WSIs) using an attention-based image sequence analyzer (ABISA). (3) Materials and methods: The presented architecture combines transformer-based self-attention mechanisms with sequential modeling using LSTM (long short-term memory) to predict the class label. This architecture leverages self-attention to capture spatial dependencies in image patches and LSTM to capture sequential dependencies across patches or frames, making it suitable for this image analysis. These two powerful combinations were integrated and applied on a custom dataset of 48 labeled WSIs (508 tiled images) generated from the highest zoom level WSI. (4) Results: The proposed ABISA algorithm attained 0.98, 1.0, and 0.98 testing accuracy, recall, and area under the curve, respectively, whereas VGG16, VGG19, and Inception V3, standard vision transformer attained testing accuracies of 0.80, 0.73, 0.82, 0.91, respectively. ABISA used 58% fewer trainable parameters than the standard vision transformer. (5) Conclusions: The proposed novel ABISA algorithm was integrated into a risk stratification pipeline to automate the detection of recurring OKC significantly faster, thus allowing the pathologist to define risk stratification faster.

To wrap or not to wrap? A study of how long words are split when reflowed on magnified web pages

AbstractMany low-vision users adjust the browser zoom level to make text more comfortable to read. Responsive websites attempt to fit the content within the viewport width, but several types of problems can potentially occur; long words may be wider than the viewport and thus partially hidden, they may cause large vertical space breaks, and words may be unnecessarily split or incorrectly split. This study set out to get insight into such word wrapping problems on responsive web pages. To help identify word wrapping issues, the tool HYPHERSPACE was developed. Experiments run on 91 websites suggest that hyphenation-related problems are prevalent. Excessive wrapping of words and overflowing words were the most notable problem on about 90% of the websites. One implication of this study is that web designers need to explicitly design for narrow viewports. The proposed tool can help web designers identify hyphenation problems on responsive web pages viewed with high magnification.

Analysis of the Possibility of Using Selected Tools and Algorithms in the Classification and Recognition of Type of Microstructure.

The aim of this research was to develop a solution based on existing methods and tools that would allow the automatic classification of selected images of cast iron microstructures. As part of the work, solutions based on artificial intelligence were tested and modified. Their task is to assign a specific class in the analyzed microstructure images. In the analyzed set, the examined samples appear in various zoom levels, photo sizes and colors. As is known, the components of the microstructure are different. In the examined photo, there does not have to be only one type of precipitate in each photo that indicates the correct microstructure of the same type of alloy, different shapes may appear in different amounts. This article also addresses the issue of data preparation. In order to isolate one type of structure element, the possibilities of using methods such as HOG (histogram of oriented gradients) and thresholding (the image was transformed into black objects on a white background) were checked. In order to avoid the slow preparation of training data, our solution was proposed to facilitate the labeling of data for training. The HOG algorithm combined with SVM and random forest were used for the classification process. In order to compare the effectiveness of the operation, the Faster R-CNN and Mask R-CNN algorithms were also used. The results obtained from the classifiers were compared to the microstructure assessment performed by experts.

Hardiness and Zoom Fatigue on Nursing Students: A Cross-Sectional Study in Indonesia During Online Learning.

The Covid-19 pandemic caused a change in learning methods to online learning. Zoom fatigue is a problem that many students experience during online learning. Zoom fatigue has an impact on physical and psychological problems so that it disrupts the online learning process. Hardiness is needed by students in order to adapt to online learning. The purpose of this study was to determine the relationship between hardiness and zoom fatigue in nursing students at Universitas Padjadjaran. This study used a quantitative approach with a cross-sectional study design. The sample technique used total sampling. Respondents were 480 nursing students at Universitas Padjadjaran. The research instrument used the Zoom Exhaustion & Fatigue Scale questionnaire and the Hardiness Instrument for the Online Learning. This study shows that almost half of the respondents have a medium hardiness level category (45%) and a zoom level of medium fatigue category (46.67%). The results of the analysis show that there is a significant negative relationship between hardiness and zoom fatigue in nursing students at Universitas Padjadjaran. The higher the student's hardiness level, the lower the student's zoom fatigue level. High hardiness can increase focus during the learning process, increase student participation, and reduce stress and fatigue in students during online learning. Lecturers and students need to collaborate to improve the learning process and repeat mental health problems during online learning. This data forms the basis for university and school nurses in providing interventions for nursing students to reduce the problem of zoom fatigue.

Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system

Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed—trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

Improving the speed and quality of cancer segmentation using lower resolution pathology images

In this paper, we propose a pipeline to investigate the performance of semantic segmentation model that employs an encoder-decoder architecture with atrous separable convolution and spatial pyramid pooling, trained on multi-resolution whole slide breast pathological images with different patch sizes. Our segmentation model obtains the best performance on zoom level 2 (10×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}magnification) with AUC score 0.974 in terms of slide-level classification. This outperforms both the performance of the pathologist and other semantic segmentation models on the Camelyon16 dataset. By offering a larger field of view and reducing noise and detail, training a semantic segmentation model on the properly selected lower resolution pathology images can further improve the precision of pixel-wise cancer region segmentation. By contrast, the corresponding inference time is 14 times shorter than the inference time trained on the highest resolution patches, and it is also shorter than the time required by a pathologist with time constraints. Moreover, we prove that the model trained on lower resolution patches can still generate refined external polygons of cancer region on the highest resolution image. This study provides new insights into efficient gigapixel histopathology analysis that will make clinical adoption more likely.

Spatial dimensions of water quality value in New England river networks

Households' willingness to pay (WTP) for water quality improvements-representing their economic value-depends on where improvements occur. Households often hold higher values for improvements close to their homes or iconic areas. Are there other areas where improvements might hold high value to individual households, do effects on WTP vary by type of improvement, and can these areas be identified even if they are not anticipated by researchers? To answer these questions, we integrated a water quality model and map-based, interactive choice experiment to estimate households' WTP for water quality improvements throughout a river network covering six New England states. The choice experiment was implemented using a push-to-web survey over a sample of New England households. Voting scenarios used to elicit WTP included interactive geographic information system (GIS) maps that illustrated three water quality measures at various zoom levels across the study domain. We captured data on how respondents maneuvered through these maps prior to answering the value-eliciting questions. Results show that WTP was influenced by regionwide quality improvements and improvements surrounding each respondent's home, as anticipated, but also by improvements in individualized locations identifiable via each respondent's map interactions. These spatial WTP variations only appear for low-quality rivers and are focused around particular areas of New England. The study shows that dynamic map interactions can convey salient information for WTP estimation and that predicting spatial WTP heterogeneity based primarily on home or iconic locations, as typically done, may overlook areas where water quality has high value.