Automatic Registration Research Articles

Automatic Registration of the Mass Spectrometry Imaging Data of Sagittal Brain Slices to the Reference Atlas.

The registration of the mass spectrometry imaging (MSI) data with mouse brain tissue slices from the atlases could perform automatic anatomical interpretation, and the comparison of MSI data in particular brain regions from different mice could be accelerated. However, the current registration of MSI data with mouse brain tissue slices is mainly focused on the coronal. Although the sagittal plane is able to provide more information about brain regions on a single histological slice than the coronal, it is difficult to directly register the complete sagittal brain slices of a mouse as a result of the more significant individualized differences and more positional shifts of brain regions. Herein, by adding the auxiliary line on the two brain regions of central canal (CC) and cerebral peduncle (CP), the registration accuracy of the MSI data with sagittal brain slices has been improved (∼2-5-folds for different brain regions). Moreover, the histological sections with different degrees deformation and different dyeing effects have been used to verify that this pipeline has a certain universality. Our method facilitates the rapid comparison of sagittal plane MSI data from different animals and accelerates the application in the discovery of disease markers.

A DC Scheme to Maximize the Benefits of the Public Sector Employees Based on Behavioral Finance

This paper introduces DB and DC pension plans, reviews the literature of DC pension plans, and puts forward three application strategies: automatic registration default contribution rate and annual automatic growth contribution rate, the risk tolerance level of different employees was evaluated by questionnaire survey, and individual investment choice of employees with different risk tolerance.

Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

Computer Image Processing System Based on Feature Extraction Technology

This article studies the methods of computer image acquisition, processing and recognition. The study found that the images collected from the computer image processing system often have blurring phenomenon. For this reason, the paper proposes a wavelet transform feature extraction algorithm based on colour images, which can separate the image and the background without greying the colour image. Through experimental research, it is found that this method can efficiently and quickly solve the problem of automatic image registration.

Peer review declaration

III International Conference on Modernization, Innovations and Progress (MIP-III-2021), 29-30 April 2021, Krasnoyarsk, Russian Federation All papers published in this volume of IOP Conference Series: Materials Science and Engineering have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Double-blind All papers came through the basic review which included an initial technical criteria check (paper field, structure of submission, adherence to the submission instructions, English language usage and a check for the similarity rate). Any papers out of the scope or containing plagiarism, including self-plagiarism, were rejected.We used a double-blind system for peer review; both reviewers’ and authors’ identities remained anonymous. The submitted papers were reviewed by at least two experts: one editorial staff member as well as at least one external reviewer. The third/fourth reviewer is involved in case two reviewers have disputes about the content of the papers or the authors do not agree with the review result. The review process took from 7 to 15 days as a rule. The reviews were conducted to the professional and scientific standards.The decision to accept or reject the paper was based on the suggestions of reviewers. Acceptance/rejecting notifications were sent to the corresponding author(s).• Conference submission management system: Conference submission is carried out through the conference management system which was developed by the Organizer. It easy to use, and has features to make it suitable for simultaneous conference calls.More than 16,500 registration forms have been already processed by the system which showed it stability and flexibility.The conference management system is integrated with the conference website of the Organizer which helps the Organizer to cope with the complexity of the submission, reviewing, refereeing and accepting processes. The current version supports:1. management of the access of reviewers and editors to papers;2. automatic registration for conferences and paper submission;3. list of the current, forthcoming and held conferences with all necessary information;4. list of all conference materials with online presentations and/or video reports of participants, list of published proceedings with direct links to publications and journals;5. sending emails to programme committee members, reviewers and authors;6. preparation and publishing of conference programmes;7. all authors submitting the papers are assumed to accept the terms of Publication Licence;8. all requirements for papers, templates and sample papers are accessible for the authors.• Number of submissions received: 502• Number of submissions sent for review: 502• Number of submissions accepted: 105• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 21 • Average number of reviews per paper: 2• Total number of reviewers involved: 45• Any additional info on review process: The Checklist for the reviewers included the following criteria: relevance to the scope of the conference topics, the scientific quality, assessing the errors, organisation of the paper, the title, the references, the level of the English language, the quality of the figures and tables, etc.The reviewers could give the following recommendation: to publish the paper as it is, to publish it after mandatory/optional minor/major revision, to reject the paper.• Contact person for queries: Anna Voroshilova, Krasnoyarsk Science and Technology City Hall, 61, Uritskogo Street, Krasnoyarsk, 660049, Russia, E-mail: krasnio@bk.ru. Tel + 7 913 538 6990

Fusion of multi-source retinal fundus images via automatic registration for clinical diagnosis

Diabetic retinopathy, age-related macular degeneration and glaucoma, are the leading causes of visual impairment or blindness of the population across different ages. Retinal fundus imaging is a clinically regular tool for the diagnosis of retinal diseases. In the interest of having a comprehensive understanding of the fundus condition, it is valuable to leverage multiple fundus images from different modalities. However, a direct fusion of the multi-source fundus images eases to mis-align the physiological structure or spatial position due to possible eyeball rotations or head movements. The problem turns out to be more severe if the images were corrupted by ill conditions on eyes, such as micro-bleeding and plaques. To tackle this problem, we propose a multi-source registration model for retinal fundus images. Our proposed method considers multiple correspondences and dual structural constraints during the registration process. The method firstly selects adequate feature points by an adjustable threshold selection strategy. Then a feature-guided correspondence estimation model is established to build complementary features. Finally, their spatial transformation is built by using mean shift evolution. The evolution is guided by Tikhonov regularization on dual geometric structures. It overcomes the mess of mean shift vector field and mitigating the ill-posed displacement in field recovery. We have conducted our method on the collected 220 multi-source retinal fundus image pairs, which involve minor and larger displacement or severe retinopathy lesions, as well as additive different intensities of Gaussian noises. Extensive experiments demonstrate that the proposed method consistently outperforms seven feature-based methods.

MO435MULTIMODAL IMAGING FOR MOLECULAR TISSUE ANALYSIS

Abstract Background and Aims MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements result in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples. For more detailed analysis of MALDI MSI data and correlation between the molecular and microscopic levels, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than by analyzing both methods individually. Therefore, MALDI MSI datasets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provides more information than a single tissue section. We have developed, established and validated an algorithm for an automatic registration of MALDI data with different histological image data for cross-process evaluation of multimodal datasets to create 3D models. This multimodal imaging approach simplifies and improves molecular analyses of tissue samples in clinical research and diagnosis. Method The datasets for fusion and creation of a 3D model consist of mass spectrometric data, histological and immunohistochemical staining methods. Histological tissue sections of a whole mouse kidney were prepared. For MALDI MSI data, organ sections were analyzed by using a Rapiflex mass-spectrometer. Results A mathematical registration was used to achieve a perfect superposition of the individual histological sections of mass spectrometric data. It is feasible to combine mass spectrometric data, histological and immunohistochemical datasets in high numbers and reconstruct the measured mouse kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distributions can be obtained. The fusion of the data also offers a virtual incision of the organ from arbitrary angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion A successful fusion of MALDI MSI data and different histological and immunohistochemical staining datasets of a whole organ is performed.

Deep learning for registration of region of interest in consecutive wireless capsule endoscopy frames

Background and objective: Functional gastrointestinal disorders (FGIDs) are reported as worldwide gastrointestinal (GI) diseases. GI motility assessment can assist the diagnosis of patients with intestine motility dysfunction. Wireless capsule endoscopy (WCE) can acquire images in the gastrointestinal (GI) tract including the small intestine where other conventional endoscopes cannot penetrate, and WCE images can reveal GI motility. To generally analyze WCE frames, the high-precision registration of consecutive WCE frames is an absolute necessity. It is difficult and meaningless to register entire WCE frames on a pixel level due to the unpredictable and massive non-rigid deformation between consecutive frames, the low quality of imaging and the complex intestinal environment. Thus, the registration of region of interest (ROI) functioning in a feature level has more significance than entire frame registration.Methods: In this paper we present Timecylce-WCE, an end-to-end automatic registration approach of ROIs on WCE images. The clinicians can determine a ROI by drawing a bounding box in any WCE frame to be registered. This proposed approach is based on a deep-learning model of time-consistency in recurrent-registering, skip-registering and self-registering cycle, and it is fully unsupervised without any label. We incorporate the global correlation map with the local correlation map in matching the features, and a novel overall loss function is designed to enable the convergence of the model. As the output, a thin-plate spline (TPS) transformed region in the template frame is highly aligned with the query ROI in a finer-grained level. To the best of our knowledge this is the first time that a deep-learning-based registration method is proposed for WCE imaging motion.Results: To highlight the effectiveness of the proposed approach, our proposed method is compared with the existing non-deep-learning methods and tested in a validation dataset with labeled matching points. The presented method resulted in the best PCK@10 (Percentage of Correct Key-points, i.e., the predicted and the true joint is within the threshold - 10 pixels) of 66.49%. We also demonstrate that variants of design improved registration accuracy.Conclusions: From the experimental analysis, it is clear that our proposed method outperforms the other existing methods. This lays the groundwork for subsequent studies, such as GI motility assessment, and WCE image synthesis.

The clinical laboratory: a decision maker hub.

We aimed to share a new laboratory model based on laboratory knowledge, meaningful use of information technology, and partnership with clinicians, to lead the appropriate use of laboratory testing and clinical decision making in the diagnosis of as-yet-undiagnosed disease. More specifically, we evaluate the role of eight different opportunistic interventions to diagnose certain asymptomatic disorders, by means of the automatic registration of appropriate laboratory testing according to different scenarios. This is a retrospective longitudinal study to evaluate the impact of laboratory interventions on the diagnosis of different diseases and on patient care, including data from January 2012 to September 2020. Overall, the above strategies have so far identified 2063 patients with clinically relevant as-yet-undiagnosed disorders who would have otherwise remained occult, suchas for instance, primary hyperparathyroidism, diabetes, and hypomagnesemia. We are facing a new laboratory model, a leading laboratory rather than a passive traditional laboratory, not just to intervene in clinical decision-making, but to make the clinical decision, through the identification of patients with occult disease.

An Automatic 3D Point Cloud Registration Method Based on Biological Vision

When measuring surface deformation, because the overlap of point clouds before and after deformation is small and the accuracy of the initial value of point cloud registration cannot be guaranteed, traditional point cloud registration methods cannot be applied. In order to solve this problem, a complete solution is proposed, first, by fixing at least three cones to the target. Then, through cone vertices, initial values of the transformation matrix can be calculated. On the basis of this, the point cloud registration can be performed accurately through the iterative closest point (ICP) algorithm using the neighboring point clouds of cone vertices. To improve the automation of this solution, an accurate and automatic point cloud registration method based on biological vision is proposed. First, the three-dimensional (3D) coordinates of cone vertices are obtained through multi-view observation, feature detection, data fusion, and shape fitting. In shape fitting, a closed-form solution of cone vertices is derived on the basis of the quadratic form. Second, a random strategy is designed to calculate the initial values of the transformation matrix between two point clouds. Then, combined with ICP, point cloud registration is realized automatically and precisely. The simulation results showed that, when the intensity of Gaussian noise ranged from 0 to 1 mr (where mr denotes the average mesh resolution of the models), the rotation and translation errors of point cloud registration were less than 0.1° and 1 mr, respectively. Lastly, a camera-projector system to dynamically measure the surface deformation during ablation tests in an arc-heated wind tunnel was developed, and the experimental results showed that the measuring precision for surface deformation exceeded 0.05 mm when surface deformation was smaller than 4 mm.

Intraoperative Computed Tomography-Based Navigation with Augmented Reality for Lateral Approaches to the Spine.

Background. Lateral approaches to the spine have gained increased popularity due to enabling minimally invasive access to the spine, less blood loss, decreased operative time, and less postoperative pain. The objective of the study was to analyze the use of intraoperative computed tomography with navigation and the implementation of augmented reality in facilitating a lateral approach to the spine. Methods. We prospectively analyzed all patients who underwent surgery with a lateral approach to the spine from September 2016 to January 2021 using intraoperative CT applying a 32-slice movable CT scanner, which was used for automatic navigation registration. Sixteen patients, with a median age of 64.3 years, were operated on using a lateral approach to the thoracic and lumbar spine and using intraoperative CT with navigation. Indications included a herniated disc (six patients), tumors (seven), instability following the fracture of the thoracic or lumbar vertebra (two), and spondylodiscitis (one). Results. Automatic registration, applying intraoperative CT, resulted in high accuracy (target registration error: 0.84 ± 0.10 mm). The effective radiation dose of the registration CT scans was 6.16 ± 3.91 mSv. In seven patients, a control iCT scan was performed for resection and implant control, with an ED of 4.51 ± 2.48 mSv. Augmented reality (AR) was used to support surgery in 11 cases, by visualizing the tumor outline, pedicle screws, herniated discs, and surrounding structures. Of the 16 patients, corpectomy was performed in six patients with the implantation of an expandable cage, and one patient underwent discectomy using the XLIF technique. One patient experienced perioperative complications. One patient died in the early postoperative course due to severe cardiorespiratory failure. Ten patients had improved and five had unchanged neurological status at the 3-month follow up. Conclusions. Intraoperative computed tomography with navigation facilitates the application of lateral approaches to the spine for a variety of indications, including fusion procedures, tumor resection, and herniated disc surgery.

Feasibility of hybrid telerehabilitation as component of a novel comprehensive care program after acute myocardial infarction in a one-year follow-up preliminary experience of a single center

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): National Health Fund Background. A novel comprehensive care program after acute myocardial infarction (AMI) „KOS-zawał" was implemented in Poland. It includes acute intervention, complex revascularization, implantation of cardiovascular electronic devices (in case of indications), rehabilitation or hybrid telerehabilitation (HTR) and scheduled outpatient follow-up. HTR is a unique component of this program. The purpose of the pilot study was to evaluate a feasibility, safety and patients’ acceptance of HTR as component of a novel care program after AMI and to assess mortality in a one-year follow-up. Methods The study included 55 patients (LVEF 55.6 ± 6.8%; aged 57.5 ± 10.5 years). Patients underwent a 5-week HTR based on Nordic walking, consisting of an initial stage (1 week) conducted within an outpatient center and a basic stage (4-week) home-based telerehabilitation five times weekly. HTR was telemonitored with a device adjusted to register electrocardiogram (ECG) recording and to transmit data via mobile phone network to the monitoring center. The moments of automatic ECG registration were pre-set and coordinated with exercise training. The influence on physical capacity was assessed by comparing changes in functional capacity (METs) from the beginning and the end of HTR. Patients filled in a questionnaire in order to assess their acceptance of HTR at the end of telerehabilitation. Results HTR resulted in a significant improvement in functional capacity and workload duration in exercise test (Table). Safety: there were neither deaths nor adverse events during HTR. Patients accepted HTR, including the need for interactive everyday collaboration with the monitoring center. Prognosis all patients survived in a one-year follow-up. Conclusions Hybrid telerehabilitation is a feasible, safe form of rehabilitation, well accepted by patients. There were no deaths in a one-year follow-up. Outcomes before and after HTR Before telerehabilitation After telerehabilitation P Exercise time [s] 381.5 ± 92.0 513.7 ± 120.2 <0.001 Maximal workload [MET] 7.9 ± 1.8 10.1 ± 2.3 <0.001 Heart rate rest [bpm] 68.6 ± 12.0 66.6 ± 10.9 0.123 Heart rate max effort [bpm] 119.7 ± 15.9 131.0 ± 20.1 <0.001 SBP rest [mmHg] 115.6 ± 14.8 117.7 ± 13.8 0.295 DBP rest [mmHg] 74.3 ± 9.2 76.2 ± 7.3 0.079 SBP max effort [mm Hg] 159.5 ± 25.7 170.7 ± 25.5 0.003 DBP max effort [mm Hg] 84.5 ± 9.2 87.2 ± 9.3 0.043 SBP systolic blood pressure, DBP diastolic blood pressure.

AFM-Based nanofabrication and quality inspection of three-dimensional nanotemplates for soft lithography

Soft lithography is one of the most promising nanofabrication technologies for scalable nanomanufacturing. It uses flexible molds or stamps to fabricate extremely high-resolution nanopatterns in a low cost and rapid fashion on both flat and curved surfaces. Although two-dimensional soft lithography has been widely investigated and applied for some applications, there is a lack of quantitative research on using nanometer scale 3D patterns fabricated by atomic force microscope (AFM) for 3D soft lithography. In this paper, we demonstrated the effectiveness of ultrasonic vibration assisted AFM-based nanomachining in fabricating 3D master nanotemplates for 3D soft lithography. We successfully applied Polydimethylsiloxane (PDMS) daughter molds, casted from a nanometer scale 3D master template, using a soft lithography technique solvent-assisted micromolding. In addition, we investigated the quality and reusability of the master nanotemplates, and the quality of the PDMS patterns by quantifying the volume differences using an automatic imaging registration algorithm. Results show that there are zero or little PDMS residuals in the polymethyl methacrylate (PMMA) master nanotemplate with a simple contour for the first and second daughter mold casting, but 7% residuals for the third casting. And a more complex master nanotemplate shows slightly higher residual levels.

Determination of underground mining-induced displacement field using multi-temporal TLS point cloud registration

This study assesses the use of terrestrial laser scanning (TLS) based automatic point cloud registration to determine the displacement field caused by underground mining. The displacement field was determined using the moving registration window technique. The study area was covered with single-family and farm buildings. The analyzes included the determination of the optimal settings of the registration algorithms and the parameters of the proposed method in terms of the accuracy of the results and the computation time. A two-step procedure for removing outliers in the displacement field was also proposed. The conducted research allowed the relationship between the values of outlier removal parameters and the density and the accuracy of the displacement field to be found. The evaluation of the final accuracy of the filtered displacement field was performed on the basis of both the displacements determined manually using the TLS-based point clouds and the displacements derived from an independent reference method.

Abstract No. 486 Augmented reality registration of three-dimensional models: a multicenter assessment using the next-generation HoloLens 2

Comparison of registration methods on HoloLens 2 for aligning 3D holographic models onto a CT grid amongst two academic centers to assess efficacy and reproducibility. A 3D holographic model of an abdominal CT phantom (CIRS 071B) with an overlying CT grid (Beekley Medical 117) was segmented using ITK-SNAP and projected using Microsoft HoloLens 2. Custom code was developed in Unity and C# for model registration using four methods: 1) one-handed gestures, 2) optional two-handed gestures, 3) Xbox controller, and 4) computer vision with automated image detection using Vuforia SDK. Code was shared and deployed across institutions. Registration times for aligning the model to the CT grid were recorded until visual satisfaction and alignment errors were < 1 cm in the x-, y-, and z-planes. Eighteen users from two academic centers (3 attendings, 3 trainees, and 3 medical students at each center) attempted each registration method using HoloLens 2 three times. Comparisons were made with paired t-tests within each center, F-tests, and unpaired t-tests between centers. Center 1 had mean manual registration times of 22.6s, 25.2s, and 77.2s, and Center 2 had times of 32.7s, 23.4s, 68.5s for one-handed gestures, two-handed gestures, and Xbox controller, respectively. Both centers showed faster registration times using one- and two-handed hand gestures compared to controller (P < 0.001 at both centers). Between centers, there was no significant difference in registration times for two-handed gestures and controller, but one-handed gestures were faster at Center 1 compared to Center 2 by 31% (P < 0.05). Both centers had fastest registration times with automated computer vision. Center 2’s mean automatic registration time was 3.4s versus Center 1’s time of 7.2s (52% reduction, P < 0.001). Registration times demonstrated similar trends within each center but showed variability in certain registration methods between centers. Prior exposure with the HoloLens may impact hologram interaction and ambient lighting conditions may affect computer vision detection. These findings encourage cross-institutional development and reinforce multicenter evaluation for new technologies, like augmented reality, in the IR suite.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Mechanical characterization of abdominal aortas using multi-perspective ultrasound imaging

Mechanical characterization of abdominal aortic aneurysms using personalized biomechanical models is being widely investigated as an alternative criterion to assess risk of rupture. These methods rely on accurate wall motion detection and appropriate model boundary conditions. In this study, multi-perspective ultrasound is combined with finite element models to perform mechanical characterization of abdominal aortas in volunteers.Multi-perspective biplane radio frequency ultrasound recordings were made under seven angles (-45° to 45°) in one phantom set-up and eight volunteers, which were merged using automatic image registration. 2-D displacement fields were estimated in the seven longitudinal ultrasound views, creating a sparse, high resolution 3-D map of the wall motion at relatively high frame rates (20–27 Hz). The displacements were used to personalize the subject-specific finite element model of which the geometry of the aorta, spine, and surrounding tissue were determined from a single 3-D ultrasound acquisition.Automatic registration of the multi-perspective images was successful in six out of eight cases with an average error of 5.4° compared to the ground truth. Displacements of the aortic wall were measured and cyclic strain of the aortic diameter was found ranging from 4.2% to 8.6%. The subject-specific mesh and inverse FE analysis was performed yielding shear moduli estimates for the wall between 104 and 215 kPa. Comparative results from a single-perspective workflow revealed very low aortic wall motion signal, which resulted in relatively high modulus estimates, between 230 and 754 kPa.Multi-perspective biplane ultrasound imaging was used to personalize finite element models of the abdominal aorta and its surroundings, and performing mechanical characterization of the aortic shear modulus. The method was found to be a more robust method compared to a single-perspective 3-D ultrasound approach. Future research will focus on investigating the use of multiple 3-D ultrasound acquisitions, the feasibility of free-hand scanning, the creation of a full 3-D automatic registration process, and with that, enable a clinical continuation of this study.

Robust Optical and SAR Image Registration Based on OS-SIFT and Cascaded Sample Consensus

Although several algorithms have achieved automatic registration on optical and synthetic aperture radar (SAR) images, it is still a challenge to establish enough reliable correspondences between such images due to their different imaging mechanisms. For this purpose, we propose a robust point-feature-based registration method. Considering the inherent properties of optical image and SAR image, two different gradient operators are utilized to construct scale spaces and extract features. A new gradient operator is defined for SAR image, yielding a more consistent gradient with optical image gradient calculated by the multiscale Sobel operator. Then, a novel cascaded matching method called cascaded sample consensus (CSC) is put forward to increase the number of correct correspondences. In the first matching, a simple but effective scale constraint strategy is used to remove outliers for a robust initial transformation model. Considering the spatial location relationship in each correct matching pair, the second matching constructs precise search spaces of the best matching points for more correspondences. Experimental results finally verify the robustness and accuracy of the proposed algorithm.

Human type 2 macrophages biologically active soluble products in the editing of stress-induced depressive-like behavior

IntroductionIn the scientific world widely discussed phenomenon of “cytokine-induced depression”. Macrophages have high plasticity and are able to control the inflammatory response; in particular, anti-inflammatory type-2 macrophages have a pronounced potential due to complex soluble factors production.ObjectivesWe have developed an original method for the type-2 macrophages generation; the resulting macrophages are characterized by the high level of a whole range of neurotrophic, neuroprotective, proangiogenic and anti-inflammatory factors production. The aim of the study was to investigate effects of human type-2 macrophages soluble products on behavioral phenotype and brain cytokines synthesis in depressive-like animals.MethodsType-2 macrophages were generated by culturing an adherent fraction of mononuclear cells with 50 ng/ml recombinant human GM-CSF in serum deprivation conditions for 7 days. (CBA x C57Bl/6)F1 depressive-like male mice, developed under the long-term social stress, were undergoing the human type-2 macrophages conditioned medium intranasal administration (60 ml twice daily for one animal) for 6 days. Mice behavioral phenotyping was carried out using an automatic registration system (Noldus Information Technology). Cytokines were determined by ELISA.ResultsDepressive-like mice behavioral phenotyping after type-2 macrophages conditioned medium administration revealed anhedonia decrease, motor activity stimulation in the open field and forced swimming tests, anxiety reduction in elevated plus maze. Behavioral changes were recorded against the pro-inflammatory cytokines (TNF-α, IL-1β, IL- 6, INFγ) decrease in striatum and hippocampus, as well as anti-inflammatory IL- 10 increase in hippocampus and hypothalamus.ConclusionsResults demonstrated the effectiveness of human type-2 macrophages biologically active soluble products in relation to the stress-induced depressive-like behavior editingDisclosureNo significant relationships.

Peer review declaration

II International Conference on Metrological Support of Innovative Technologies (ICMSIT-II-2021), 3-6 March 2021, Krasnoyarsk, Russian Federation All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Double-blind All papers came through the basic review which included an initial technical criteria check (paper field, structure of submission, adherence to the submission instructions, English language usage and a check for the similarity rate). Any papers out of the scope or containing plagiarism, including self-plagiarism, were rejected.We used a double-blind system for peer review; both reviewers’ and authors’ identities remained anonymous. The submitted papers were reviewed by at least two experts: one editorial staff member as well as at least one external reviewer. The third/fourth reviewer is involved in case two reviewers have disputes about the content of the papers or the authors do not agree with the review result. The review process took from 7 to 15 days as a rule. The reviews were conducted to the professional and scientific standards.The decision to accept or reject the paper was based on the suggestions of reviewers. Acceptance/rejecting notifications were sent to the corresponding author(s).• Conference submission management system: Conference submission is carried out through the conference management system which was developed by the Organizer. It easy to use, and has features to make it suitable for simultaneous conference calls.More than 15,500 registration forms have been already processed by the system which showed it stability and flexibility.The conference management system is integrated with the conference website of the Organizer which helps the Organizer to cope with the complexity of the submission, reviewing, refereeing and accepting processes. The current version supports:1. management of the access of reviewers and editors to papers;2. automatic registration for conferences and paper submission;3. list of the current, forthcoming and held conferences with all necessary information;4. list of all conference materials with online presentations and/or video reports of participants, list of published proceedings with direct links to publications and journals;5. sending emails to programme committee members, reviewers and authors;6. preparation and publishing of conference programmes;7. all authors submitting the papers are assumed to accept the terms of Publication Licence;8. all requirements for papers, templates and sample papers are accessible for the authors.• Number of submissions received: 535 • Number of submissions sent for review: 535 • Number of submissions accepted: 315 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 58 • Average number of reviews per paper: 2 • Total number of reviewers involved: 47 • Any additional info on review process: The Checklist for the reviewers included the following criteria: relevance to the scope of the conference topics, the scientific quality, assessing the errors, organisation of the paper, the title, the references, the level of the English language, the quality of the figures and tables, etc.The reviewers could give the following recommendation: to publish the paper as it is, to publish it after mandatory/optional minor/major revision, to reject the paper.• Contact person for queries: Anna Voroshilova, Krasnoyarsk Science and Technology City Hall, 61, Uritskogo Street, Krasnoyarsk, 660049, Russia, E-mail: krasnio@bk.ru. Tel + 7 913 538 6990