Subsequent Registration Research Articles

Culture system for longitudinal monitoring of bone dynamics ex vivo.

To quantify and visualize both bone formation and resorption within osteochondral explants cultured ex vivo is challenging with the current analysis techniques. An approach that enables monitoring of bone remodeling dynamics is longitudinal microcomputed tomography (µCT), a non-destructive technique that relies on repeated µCT scanning and subsequent registration of consecutive scans. In this study, a two-compartment culture system suitable for osteochondral explants that allowed for µCT scanning during ex vivo culture was established. Explants were scanned repeatedly in a fixed orientation, which allowed assessment of bone remodeling due to adequate image registration. Using this method, bone formation was found to be restricted to the outer surfaces when cultured statically. To demonstrate that the culture system could capture differences in bone remodeling, explants were cultured statically and under dynamic compression as loading promotes osteogenesis. No quantitative differences between static and dynamic culture were revealed. Still, only in dynamic conditions, bone formation was visualized on trabecular surfaces located within the inner cores, suggesting enhanced bone formation towards the center of the explants upon mechanical loading. Taken together, the ex vivo culture system in combination with longitudinal µCT scanning and subsequent registration of images demonstrated potential for evaluating bone remodeling within explants.

Getting the Dose Right in Drug Development for Rare Diseases: Barriers and Enablers.

In the relentless pursuit of optimizing drug development, the intricate process of determining the ideal dosage unfolds. This involves "dose-finding" studies, crucial for providing insights into subsequent registration trials. However, the challenges intensify when tackling rare diseases. The complexity arises from poorly understood pathophysiologies, scarcity of appropriate animal models, and limited natural history understanding. The inherent heterogeneity, coupled with challenges in defining clinical end points, poses substantial challenges, hindering the utility of available data. The small affected population, low disease awareness, and restricted healthcare access compound the difficulty in conducting dose-finding studies. This white paper delves into critical dose selection aspects, focusing on key therapeutic areas, such as oncology, neurology, hepatology, metabolic rare diseases. It also explores dose selection challenges posed by pediatric rare diseases as well as novel modalities, including enzyme replacement therapies, cell and gene therapies, and oligonucleotides. Several examples emphasize the pivotal role of clinical pharmacology in navigating the complexities associated with these diseases and emerging treatment modalities.

An LADM-based profile for marine geo-regulation: A case study of China

With the rapid development of the marine economy, the transfer, lease and other transactions of marine use right in China's coastal areas have emerged continuously. At the same time, to promote the effective protection of marine environment and resources, the establishment and reform of marine property rights system has become a very important part. To play the basic role of the market and environmental protection in the utilization of marine resources, it is very important to establish the corresponding property right registration system. In response, the central government has proposed the establishment of a unified registration system for real estate and natural resources nationwide, including in marine areas. Therefore, this study takes China's marine administration as the research object and constructs a conceptual model based on LADM, which serves as an important basis for the subsequent registration system development. Specifically, this study first summarizes the evolution process of China's marine administration system since the founding of China. Secondly, it puts forward the existing marine rights system. Then, based on the LADM standard and the instance-level diagram for marine administration activities, the corresponding LADM-based profile is developed. The core improvement of this model lies in the improved structuring of marine-related rights, restrictions and responsibilities (as well as related source documents), which is an important preparation for the expansion of land information infrastructure to the sea.

Nudging the nudger: Performance feedback and organ donor registrations

In a randomized controlled trial conducted in three waves over 2.5 years and involving nearly 700 customer-service representatives (CSRs) from a Canadian government service agency, we studied how providing CSRs with repeated performance feedback, with or without peer comparison, affected their subsequent organ donor registration rates. The feedback resulted in a 25 % increase in daily signups compared to otherwise equivalent encouragements and reminders. Adding benchmark information about peer performance did not amplify or diminish this effect. We observed increased registration rates for both high and low performers. A post-intervention survey indicates that CSRs in all conditions found the information included in the treatments helpful and motivating, and that signing up organ donors makes their job more meaningful. We also found suggestive evidence that performance feedback with benchmark information was the most motivating and created the least pressure to perform.

The perspective of the logarithmic field sprayer technology.

A logarithmic sprayer was suggested about 70 years ago, but it has not yet been seriously used in research and development, and subsequent registration of plant protection products. Logarithmic sprayers have resorted to mere demonstration experiments to show end users and others how plant protection products work. Fitting dose-response curves in field experiments, however, generates much essential information, e.g., extraction of various effective field rate levels (e.g., ED20, ED50, and ED80). One of the reasons for it rarely being used in the registration of plant protection products is that the dose-response curve regression was hitherto difficult to fit; the registration requirement solely focuses on analyses of variance. Another alleged obstacle is that the logarithmic plots have systematically, not randomly distributed field rates. This paper goes through some of the problems of how to non-randomly analyze field rates by taking autocorrelation into account to make the logarithmic sprayer palatable as registration documentation by assessing efficacy, selectivity, environmental side effects, general toxicity of plant protection products, and cost-effectiveness. The development in precision agriculture, drone technology, and automation of data capture and subsequent analysis could make the logarithmic sprayer a cost-effective alternative to numerous ANOVA experiments with very few fixed field rates to aid the precision spraying of pesticides and thus reduce unnecessary environmental side effects. © 2024 Society of Chemical Industry.

Design of AR system tracking registration method using dynamic target light-field

In the process of tracking registration for an augmented reality (AR) system, it's essential first to obtain the system's initial state, as its accuracy significantly influences the precision of subsequent three-dimensional tracking registration. At this point, minor movements of the target can directly lead to calibration errors. Current methods fail to address the challenge of capturing the initial state of dynamic deformation in optically transparent AR systems effectively. To tackle this issue, the concept of a static light-field is expanded to a four-dimensional dynamic light-field, and a tracking registration method for an optical see-through AR system based on the four-dimensional dynamic light-field is introduced. This method begins by analyzing the relationship between the components of the optical see-through AR system and studying the impact of a dynamic target on the initial state model. Leveraging the fundamental principle of light-field correlation, the theory and model for four-dimensional dynamic light-field tracking registration are developed. A lot of experiments have confirmed the algorithm's accuracy, enhanced its stability, and demonstrated the superior performance of the three-dimensional tracking registration algorithm.

Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas.

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Comprehensive 3D Analysis of the Renal System and Stones: Segmenting and Registering Non-Contrast and Contrast Computed Tomography Images

AbstractDetecting and accurately locating kidney stones, which are common urological conditions, can be challenging when using imaging examinations. Therefore, the primary objective of this research is to develop an ensemble model that integrates segmentation and registration techniques. This model aims to visualize the inner structure of the kidney and accurately identify any underlying kidney stones. To achieve this, three separate datasets, namely non-contrast computed tomography (CT) scans, corticomedullary CT scans, and CT excretory scans, are annotated to enhance the three-dimensional (3D) reconstruction of the kidney’s complex anatomy. Initially, the research focuses on utilizing segmentation models to identify and annotate specific classes within the annotated datasets. Subsequently, a registration algorithm is employed to align and combine the segmented results, resulting in a comprehensive 3D representation of the kidney’s anatomical structure. Three cutting-edge segmentation algorithms are employed and evaluated during the segmentation phase, with the most accurate segments being selected for the subsequent registration process. Ultimately, the registration process successfully aligns the kidneys across all three phases and combines the segmented labels, producing a detailed 3D visualization of the complete kidney structure. For kidney segmentation, Swin UNETR exhibited the highest Dice score of 95.21%; for stone segmentation, ResU-Net achieved the highest Dice score of 87.69%. Regarding Artery, Cortex, and Medulla segmentation, ResU-Net and 3D U-Net show comparable performance with similar Dice scores. Considering the Collecting System and Parenchyma, ResU-Net and 3D U-Net demonstrate similar performance in Dice scores. In conclusion, the proposed ensemble model shows potential in accurately visualizing the internal structure of the kidney and precisely localizing kidney stones. This advancement improves the diagnosis process and preoperative planning in percutaneous nephrolithotomy.

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells.

Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers-all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.

Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer.

The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support.

The joint effects of genetic liability and the death of close relatives on risk for major depression and alcohol use disorder in a Swedish national sample.

To determine whether genetic risk factors for major depression (MD) and alcohol use disorder (AUD) interact with a potent stressor - death of spouse, parent, and sibling - in predicting episodes of, respectively, MD and AUD. MD and AUD registrations were assessed from national Swedish registries. In individuals born in Sweden 1960-1970, we identified 7586, 388 459, and 34 370 with the loss of, respectively, a spouse, parent, and sibling. We started following subjects at age 18 or the year 2002 with end of follow-up in 2018. We examined time to event - a registration for MD within 6 months or AUD within a year - on an additive scale, using the Nelson-Aalen estimator. Genetic risk was assessed by the Family Genetic Risk Score (FGRS). In separate models controlling for the main effects of death of spouse, parent, and sibling, FGRS, and sex, significant interactions were seen in all analyses between genetic risk for MD and death of relative in prediction of subsequent MD registration. A similar pattern of results, albeit with weaker interaction effects, was seen for genetic risk for AUD and risk for AUD registration. Genetic risk for bipolar disorder (BD) and anxiety disorders (AD) also interacted with event exposure in predicting MD. Genetic risk for both MD and AUD act in part by increasing the sensitivity of individuals to the pathogenic effects of environmental stressors. For prediction of MD, similar effects are also seen for genetic risk for AD and BD.

GRiD: Guided Refinement for Detector-Free Multimodal Image Matching.

Multimodal image matching is essential in image stitching, image fusion, change detection, and land cover mapping. However, the severe nonlinear radiometric distortion (NRD) and geometric distortions in multimodal images severely limit the accuracy of multimodal image matching, posing significant challenges to existing methods. Additionally, detector-based methods are prone to feature point offset issues in regions with substantial modal differences, which also hinder the subsequent fine registration and fusion of images. To address these challenges, we propose a guided refinement for detector-free multimodal image matching (GRiD) method, which weakens feature point offset issues by establishing pixel-level correspondences and utilizes reference points to guide and correct matches affected by NRD and geometric distortions. Specifically, we first introduce a detector-free framework to alleviate the feature point offset problem by directly finding corresponding pixels between images. Subsequently, to tackle NRD and geometric distortion in multimodal images, we design a guided correction module that establishes robust reference points (RPs) to guide the search for corresponding pixels in regions with significant modality differences. Moreover, to enhance RPs reliability, we incorporate a phase congruency module during the RPs confirmation stage to concentrate RPs around image edge structures. Finally, we perform finer localization on highly correlated corresponding pixels to obtain the optimized matches. We conduct extensive experiments on four multimodal image datasets to validate the effectiveness of the proposed approach. Experimental results demonstrate that our method can achieve sufficient and robust matches across various modality images and effectively suppress the feature point offset problem.

M. И. Воротынский, Новосильский град и немилость государя

Introduction. The events of the second half of the XVI century, which unfolded around the town of Novosil, have a close connection with both the domestic and foreign policy situation that was relevant at that time. They were also connected with a number of not quite obvious aspects of the financial, administrative, status position of Prince Michael Vorotynsky. Tsar John Vasilyevich perceived him negatively, feared him as a potential "troublemaker", despite the obvious loyalty of the prince. This had a significant impact on the fate of this outstanding Russian politician and military commander and, as it seems, was closely connected with the fate of the family heritage of the Novosilsk lords. Materials and methods. The texts of the wills of Tsar Ivan Vasilyevich and Prince Michael Vorotynsky, the messages to the prince and the tsar's reply messages on behalf of the prince to the leaders of Poland and Lithuania, information from Russian chronicles, data on the formation of military contingents, information of a historical and linguistic nature and the results of research on the era in question were used as source materials for the study. In this work, comparative-historical and system-analytical methods were used using an interdisciplinary approach. Results. The transfer of family lands into the possession of Michael Vorotynsky, issued as a "land exchange" with the sovereign, combined with the confiscation of property and the need to restore fortifications, as well as maintaining their combat readiness in the conditions of Crimean Tatar activity and the Livonian War, led to the actual ruin of the prince. The analysis of the published epistolary heritage of the epoch makes it possible to identify flaws in the generally accepted translation of the "correspondence of Ivan the Terrible" and to evaluate the semantic shades conveyed in it closely related to the city of Novosil in a slightly different way. Conclusions. The transfer, restoration and maintenance of the fortress of Novosilsk and a number of other fortifications at nonstate expense, the strengthening of the defense capability of the southern borders and the subsequent foreign policy epistolary registration of the status associated with them were part of a long intrigue against Prince Michael Vorotynsky, the author of which could be the Tsar of Russia Ivan Vasilyevich.

DTR-GAN: An Unsupervised Bidirectional Translation Generative Adversarial Network for MRI-CT Registration

Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named DTR-GAN. Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the complementary information of different modalities. Then, to enhance the quality of the transformed images in the translation network, we design a multiscale encoder–decoder network that effectively captures both local and global features in images. Finally, we propose a mixed similarity loss to encourage the warped image to be closer to the target image in deep features. We extensively evaluate methods for MRI-CT image registration tasks of the abdominal cavity with advanced unsupervised multimodal image registration approaches. The results indicate that DTR-GAN obtains a competitive performance compared to other methods in MRI-CT registration. Compared with DFR, DTR-GAN has not only obtained performance improvements of 2.35% and 2.08% in the dice similarity coefficient (DSC) of MRI-CT registration and CT-MRI registration on the Learn2Reg dataset but has also decreased the average symmetric surface distance (ASD) by 0.33 mm and 0.12 mm on the Learn2Reg dataset.

Coarse-to-fine registration and time-intensity curves constraint for liver DCE-MRI synthesis

Image registration plays a crucial role in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), used as a fundamental step for the subsequent diagnosis of benign and malignant tumors. However, the registration process encounters significant challenges due to the substantial intensity changes observed among different time points, resulting from the injection of contrast agents. Furthermore, previous studies have often overlooked the alignment of small structures, such as tumors and vessels. In this work, we propose a novel DCE-MRI registration framework that can effectively align the DCE-MRI time series. Specifically, our DCE-MRI registration framework consists of two steps, i.e., a de-enhancement synthesis step and a coarse-to-fine registration step. In the de-enhancement synthesis step, a disentanglement network separates DCE-MRI images into a content component representing the anatomical structures and a style component indicating the presence or absence of contrast agents. This step generates synthetic images where the contrast agents are removed from the original images, alleviating the negative effects of intensity changes on the subsequent registration process. In the registration step, we utilize a coarse registration network followed by a refined registration network. These two networks facilitate the estimation of both the coarse and refined displacement vector fields (DVFs) in a pairwise and groupwise registration manner, respectively. In addition, to enhance the alignment accuracy for small structures, a voxel-wise constraint is further conducted by assessing the smoothness of the time-intensity curves (TICs). Experimental results on liver DCE-MRI demonstrate that our proposed method outperforms state-of-the-art approaches, offering more robust and accurate alignment results.

Application of bioinformatics methods in pesticide research and development

The use of pesticides is accompanied by a number of environmental and agrotechnological problems. Many pesticides do not degrade and stay in the soil for years and have low selectivity. Massive application of pesticides with non-selective nature of action caused a gradual increase of resistance in pests due to persistent inherited changes in their DNA. This affects the efficiency of growing agricultural plants and pollution of the environment and food. Computational biology methods, which are actively developing all over the world, can help to solve this problem. Despite the fact that in Russia bioinformatics methods are used to study plant genes of animals, metagenomes of microorganisms, there are no own databases and specialized computer applications for such research and pesticide modernization. Development of domestic similar bioinformatics tools is also an urgent task. The article highlights the problem of creating new effective and environmentally friendly pesticides. The methods of bioinformatics that can be used in the research and development of pesticides are given. The stages of creating new pesticides by bioinformatics methods (review of databases, modeling of molecules, modeling of the interaction of a pesticide with a target, prediction of biological activity) are considered. A description is given of the methods for optimizing the molecular framework of pesticides, which is a change in the carbon skeleton in order to search for new active compounds and screen out many similar compounds in the chemical space. Foreign web resources used to assess the presence of pesticidal properties in substances, such as toxicity, metabolism and physico-chemical properties, and their subsequent registration as pesticides are given.

Periradicular surgery: a longitudinal registry study of ten-year outcomes and factors predictive of post-surgical extraction.

This historical prospective cohort study of the adult population of Sweden is based on data from a national registry: the primary aim was to evaluate the long-term survival of teeth after periradicular surgery. A secondary aim was to identify factors predictive of extraction within 10 years of registration of periradicular surgery. The cohort consisted of all individuals who had undergone periradicular surgery to treat apical periodontitis, as reported to the Swedish Social Insurance Agency (SSIA) during 2009. The cohort was followed until December 31, 2020. Subsequent registrations of extractions were collected for Kaplan-Meier survival analyses and survival tables. The patients' sex, age, dental service provider and tooth group were also retrieved from SSIA. Only one tooth per individual was included in the analyses. Multivariable regression analysis was used and P < 0.05 was considered statistically significant. The reporting guidelines STROBE and PROBE were followed. After data cleaning, and exclusion of 157 teeth, 5 622 teeth/individuals remained for analysis. The mean age of the individuals at the time of the periradicular surgery was 60.5 years (range 20-97, standard deviation 13.31); 55% were women. At the end of the follow-up, that is, up to 12 years, a total of 34.1% of the teeth had been reported as extracted. The multivariate logistic regression analysis, based on follow-up data at 10 years after registration of the periradicular surgery, included 5 548 teeth, of which 1 461 (26.3%) had been extracted. Significant associations between the independent variables tooth group and dental care setting (both P < 0.001) and the dependent variable extraction were found. The highest odds ratio (OR) for extraction applied to tooth group: compared to maxillary incisors and canines, mandibular molars were at greatest risk of extraction (OR 2.429, confidence interval 1.975-2.987, P < 0.001). After periradicular surgery in predominantly elderly people in Sweden, approximately three quarters of the teeth are retained over a 10-year period. The type of tooth is associated with extraction: mandibular molars are at greater risk of extraction than maxillary incisors and canines.

A New Approach toward Corner Detection for Use in Point Cloud Registration

For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising 104 points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising 106 points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications.

Automated signal intensity analysis of the spinal cord for detection of degenerative cervical myelopathy — a matched-pair MRI study

PurposeDetection of T2 hyperintensities in suspected degenerative cervical myelopathy (DCM) is done subjectively in clinical practice. To gain objective quantification for dedicated treatment, signal intensity analysis of the spinal cord is purposeful. We investigated fully automated quantification of the T2 signal intensity (T2-SI) of the spinal cord using a high-resolution MRI segmentation.MethodsMatched-pair analysis of prospective acquired cervical 3D T2-weighted sequences of 114 symptomatic patients and 88 healthy volunteers. Cervical spinal cord was segmented automatically through a trained convolutional neuronal network with subsequent T2-SI registration slice-by-slice. Received T2-SI curves were subdivided for each cervical level from C2 to C7. Additionally, all levels were subjectively classified concerning a present T2 hyperintensity. For T2-positive levels, corresponding T2-SI curves were compared to curves of age-matched volunteers at the identical level.ResultsForty-nine patients showed subjective T2 hyperintensities at any level. The corresponding T2-SI curves showed higher signal variabilities reflected by standard deviation (18.51 vs. 7.47 a.u.; p < 0.001) and range (56.09 vs. 24.34 a.u.; p < 0.001) compared to matched controls. Percentage of the range from the mean absolute T2-SI per cervical level, introduced as “T2 myelopathy index” (T2-MI), was correspondingly significantly higher in T2-positive segments (23.99% vs. 10.85%; p < 0.001). ROC analysis indicated excellent differentiation for all three parameters (AUC 0.865–0.920).ConclusionThis fully automated T2-SI quantification of the spinal cord revealed significantly increased signal variability for DCM patients compared to healthy volunteers. This innovative procedure and the applied parameters showed sufficient diagnostic accuracy, potentially diagnosing radiological DCM more objective to optimize treatment recommendation.Trial registrationDRKS00012962 (17.01.2018) and DRKS00017351 (28.05.2019)

RHEMAT: Robust human ear based multimodal authentication technique

Ear biometrics is a fast-developing field of computer vision that offers several advantages over existing biometric authentication methods. The performance of the ear is superior to or comparable to that of other biometric traits. However, the recognition performance of 2D ear images degrades due to the impacts of posture, illumination, and scaling. To overcome these challenges, we propose combining 3D and 2D ear images and constructing a keypoint detector and descriptor using a covariance matrix derived from texture and geometry information. The feature vector of the descriptor is then used to describe keypoints and utilized to identify keypoints with the smallest distance between a probe and gallery for the subsequent registration process. The registration error that results serves as the matching score. The proposed method is evaluated on a large database of ear images and compared to state-of-the-art techniques, demonstrating superior performance in occlusion, noise, and pose variations.