Search

BACKGROUND: Left atrial (LA) speckle tracking provides detailed information on atrial function. Its utility for predicting subclinical atrial fibrillation (SCAF) is unclear. Therefore, we sought to investigate whether LA strain measures could predict SCAF detected by long-term continuous rhythm monitoring. METHODS: This was an echocardiographic substudy of the LOOP study, where elderly at risk of stroke were randomized to receive a loop recorder (Reveal LINQ) or control. Participants who received a loop recorder were included in this analysis. Echocardiography included LA reservoir, conduit, and contraction strain. Participants were followed with continuous rhythm monitoring for SCAF (≥6 minutes). Cox proportional hazards regression was applied to adjust for atrial fibrillation risk factors. RESULTS: In total, 956 participants were eligible for analysis. Median continuous rhythm monitoring was 35 months (IQR, 20–40 months), during which 278 (29%) were diagnosed with SCAF. The mean age was 74 years, 56% were male, median CHA2DS2-VASc-score was 4. LA reservoir strain was an independent predictor of SCAF after multivariable adjustments (HR, 1.04 [1.02–1.05], per 1% decrease) and so was contraction strain. The findings were unchanged in competing risk analyses and in participants with normal LA size and diastolic function. Participants with low reservoir strain (<33%) had a significantly higher risk of SCAF compared with those with high reservoir strain (incidence rate, 14.5 [12.4–16.9] versus 9.8 [8.2–11.8] events/100 person-years). The same was noted for low versus high contraction strain. CONCLUSIONS: LA reservoir and contraction strain are independent predictors of SCAF in elderly at risk of stroke. This also applies to individuals with normal LA size and diastolic function. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02036450., BACKGROUND: Left atrial (LA) speckle tracking provides detailed information on atrial function. Its utility for predicting subclinical atrial fibrillation (SCAF) is unclear. Therefore, we sought to investigate whether LA strain measures could predict SCAF detected by long-term continuous rhythm monitoring. METHODS: This was an echocardiographic substudy of the LOOP study, where elderly at risk of stroke were randomized to receive a loop recorder (Reveal LINQ) or control. Participants who received a loop recorder were included in this analysis. Echocardiography included LA reservoir, conduit, and contraction strain. Participants were followed with continuous rhythm monitoring for SCAF (≥6 minutes). Cox proportional hazards regression was applied to adjust for atrial fibrillation risk factors. RESULTS: In total, 956 participants were eligible for analysis. Median continuous rhythm monitoring was 35 months (IQR, 20-40 months), during which 278 (29%) were diagnosed with SCAF. The mean age was 74 years, 56% were male, median CHA2DS2-VASc-score was 4. LA reservoir strain was an independent predictor of SCAF after multivariable adjustments (HR, 1.04 [1.02-1.05], per 1% decrease) and so was contraction strain. The findings were unchanged in competing risk analyses and in participants with normal LA size and diastolic function. Participants with low reservoir strain (<33%) had a significantly higher risk of SCAF compared with those with high reservoir strain (incidence rate, 14.5 [12.4-16.9] versus 9.8 [8.2-11.8] events/100 person-years). The same was noted for low versus high contraction strain. CONCLUSIONS: LA reservoir and contraction strain are independent predictors of SCAF in elderly at risk of stroke. This also applies to individuals with normal LA size and diastolic function. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02036450.

Dynamic 3D imaging is increasingly used to study evolving objects. We address the problem of detecting and tracking simple objects that merge or split in time. Common solutions involve detecting topological changes. Instead, we solve the problem in 4D by exploiting the observation that if objects only merge or only split, they appear as a single component in 4D. This allows us to initiate a topologically simple 3D hypersurface and deform it to fit the surface of all objects at all times. This gives an extremely compact representation of the objects’ evolution. We test our method on artificial 4D images and compare it to other segmentation methods. We also apply our method to a 4D X-ray data set to quantify evolving topology. Our method performs comparably to existing methods with better resource use and improved robustness.

Conducting cellulose composites are promising sustainable functional materials that have found application in energy devices, sensing and water purification. Herein, conducting aerogels are fabricated based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, using the ice templating technique, and their bulk morphology is characterized with X-ray microtomography. The freezing method (-20 degrees C in a freezer vs liquid nitrogen) does not impact the mean porosity of the aerogels but the liquid-N2 aerogels have smaller pores. The integration of carbon fibers as addressing electrodes prior to freezing results in increased mean porosity and pore size in the liquid-N2 aerogels signifying that the carbon fibers alter the morphology of the aerogels when the freezing is fast. Spatially resolved porosity and pore size distributions also reveal that the liquid-N2 aerogels are more inhomogeneous. Independent of the freezing method, the aerogels have similar electrochemical properties. For aerogels without carbon fibers, freezer-aerogels have higher compression modulus and are less stable under cycling compression fatigue test. This can be explained by higher porosity with larger pores in the center of liquid-N2 aerogels and thinner pore walls. This work demonstrates that micro-CT is a powerful tool for characterizing the morphology of aerogels in a non-destructive and spatially resolved manner. Conducting aerogels based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate are fabricated with the ice templating technique and their bulk morphology is characterized in a spatially resolved manner with X-ray microtomography. The effect of the freezing temperature and the integration of carbon fibers electrodes prior to freezing on the morphology, mechanical, and electrochemical properties is examined., Funding Agencies|Wallenberg Wood Science Center [KAW 2018.0452]; European Unions Horizon 2020 research and innovation programme (FET-OPEN-HyPhOE) [800926]; Swedish Research Council [VR-2017-04910, VR 2022-03507]; Swedish Foundation for Strategic Research [ID17-0097, FFL-18-0101]; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]

Conducting cellulose composites are promising sustainable functional materials that have found application in energy devices, sensing and water purification. Herein, conducting aerogels are fabricated based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, using the ice templating technique, and their bulk morphology is characterized with X-ray microtomography. The freezing method (-20 degrees C in a freezer vs liquid nitrogen) does not impact the mean porosity of the aerogels but the liquid-N2 aerogels have smaller pores. The integration of carbon fibers as addressing electrodes prior to freezing results in increased mean porosity and pore size in the liquid-N2 aerogels signifying that the carbon fibers alter the morphology of the aerogels when the freezing is fast. Spatially resolved porosity and pore size distributions also reveal that the liquid-N2 aerogels are more inhomogeneous. Independent of the freezing method, the aerogels have similar electrochemical properties. For aerogels without carbon fibers, freezer-aerogels have higher compression modulus and are less stable under cycling compression fatigue test. This can be explained by higher porosity with larger pores in the center of liquid-N2 aerogels and thinner pore walls. This work demonstrates that micro-CT is a powerful tool for characterizing the morphology of aerogels in a non-destructive and spatially resolved manner. Conducting aerogels based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate are fabricated with the ice templating technique and their bulk morphology is characterized in a spatially resolved manner with X-ray microtomography. The effect of the freezing temperature and the integration of carbon fibers electrodes prior to freezing on the morphology, mechanical, and electrochemical properties is examined., Funding Agencies|Wallenberg Wood Science Center [KAW 2018.0452]; European Unions Horizon 2020 research and innovation programme (FET-OPEN-HyPhOE) [800926]; Swedish Research Council [VR-2017-04910, VR 2022-03507]; Swedish Foundation for Strategic Research [ID17-0097, FFL-18-0101]; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]

Conducting cellulose composites are promising sustainable functional materials that have found application in energy devices, sensing and water purification. Herein, conducting aerogels are fabricated based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, using the ice templating technique, and their bulk morphology is characterized with X-ray microtomography. The freezing method (-20 degrees C in a freezer vs liquid nitrogen) does not impact the mean porosity of the aerogels but the liquid-N2 aerogels have smaller pores. The integration of carbon fibers as addressing electrodes prior to freezing results in increased mean porosity and pore size in the liquid-N2 aerogels signifying that the carbon fibers alter the morphology of the aerogels when the freezing is fast. Spatially resolved porosity and pore size distributions also reveal that the liquid-N2 aerogels are more inhomogeneous. Independent of the freezing method, the aerogels have similar electrochemical properties. For aerogels without carbon fibers, freezer-aerogels have higher compression modulus and are less stable under cycling compression fatigue test. This can be explained by higher porosity with larger pores in the center of liquid-N2 aerogels and thinner pore walls. This work demonstrates that micro-CT is a powerful tool for characterizing the morphology of aerogels in a non-destructive and spatially resolved manner. Conducting aerogels based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate are fabricated with the ice templating technique and their bulk morphology is characterized in a spatially resolved manner with X-ray microtomography. The effect of the freezing temperature and the integration of carbon fibers electrodes prior to freezing on the morphology, mechanical, and electrochemical properties is examined., Funding Agencies|Wallenberg Wood Science Center [KAW 2018.0452]; European Unions Horizon 2020 research and innovation programme (FET-OPEN-HyPhOE) [800926]; Swedish Research Council [VR-2017-04910, VR 2022-03507]; Swedish Foundation for Strategic Research [ID17-0097, FFL-18-0101]; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

SUper-Resolution ultrasound imaging using the Erythrocytes (SURE) can visualize blood vessels at about 25 to 50 µm resolution, but validation is required to assess how accurately the vasculature and its morphology are represented. Previous work compared ultrasound imaging to maximum intensity projections (MIP) of micro-CT volumes at an insufficient voxel size of 22.6 µm. Here, a 5 µm voxel size micro-CT volume of a cortical region in an excised rat kidney was acquired to test the hypothesis that blood vessels down to 50 µm in diameter are detected with SURE imaging. For the micro-CT volume, the blood vessels were segmented by an intensity threshold, and local thickness estimates were computed using expanding spheres within the segmentation mask. An affine registration between the SURE image and the micro-CT volume was manually defined, and a MIP of the microCT volume across 2 mm from the ultrasound imaging plane was computed for vessels with diameter estimates greater than 50 µm. The SURE image depicts 12 cortical radial vessels with high intensities and 9 dimmer cortical radial vessels. Notably, the 12 high intensity vessels are also depicted in the microCT projection, but 5 cortical radial vessels and 2 arcuate blood vessels are only visible in the SURE image. Of the faintly depicted cortical radial vessels, 5 vessels are not readily matched to vessels in the micro-CT projection. On the other hand, the microCT projection contains 6 cortical radial vessel segments and 2 arcuate blood vessels not depicted in the SURE image. These discrepancies in the comparison may arise from the challenging registration of the ultrasound focus beam and the micro-CT volume that is complicated further by the different states of the imaged tissue. Despite these challenges, SURE depicts most vessels at least down to 50 µm and even resolves parallel vessels not visible in the micro-CT projection, thus highlighting its clinical potential.

Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf

Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements. With more than 176,000 3D poses from 24 different subjects performing 5 different sports activities, SportsPose provides a diverse and comprehensive set of 3D poses that reflect the complex and dynamic nature of sports movements. Contrary to other markerless datasets we have quantitatively evaluated the precision of SportsPose by comparing our poses with a commercial marker-based system and achieve a mean error of 34.5 mm across all evaluation sequences. This is comparable to the error reported on the commonly used 3DPW dataset. We further introduce a new metric, local movement, which describes the movement of the wrist and ankle joints in relation to the body. With this, we show that SportsPose contains more movement than the Human3.6M and 3DPW datasets in these extremum joints, indicating that our movements are more dynamic. The dataset with accompanying code can be downloaded from our website. We hope that SportsPose will allow researchers and practitioners to develop and evaluate more effective models for the analysis of sports performance and injury prevention. With its realistic and diverse dataset, SportsPose provides a valuable resource for advancing the state-of-the-art in pose estimation in sports.

Conducting cellulose composites are promising sustainable functional materials that have found application in energy devices, sensing and water purification. Herein, conducting aerogels are fabricated based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, using the ice templating technique, and their bulk morphology is characterized with X-ray microtomography. The freezing method (-20 degrees C in a freezer vs liquid nitrogen) does not impact the mean porosity of the aerogels but the liquid-N2 aerogels have smaller pores. The integration of carbon fibers as addressing electrodes prior to freezing results in increased mean porosity and pore size in the liquid-N2 aerogels signifying that the carbon fibers alter the morphology of the aerogels when the freezing is fast. Spatially resolved porosity and pore size distributions also reveal that the liquid-N2 aerogels are more inhomogeneous. Independent of the freezing method, the aerogels have similar electrochemical properties. For aerogels without carbon fibers, freezer-aerogels have higher compression modulus and are less stable under cycling compression fatigue test. This can be explained by higher porosity with larger pores in the center of liquid-N2 aerogels and thinner pore walls. This work demonstrates that micro-CT is a powerful tool for characterizing the morphology of aerogels in a non-destructive and spatially resolved manner. Conducting aerogels based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate are fabricated with the ice templating technique and their bulk morphology is characterized in a spatially resolved manner with X-ray microtomography. The effect of the freezing temperature and the integration of carbon fibers electrodes prior to freezing on the morphology, mechanical, and electrochemical properties is examined., Funding Agencies|Wallenberg Wood Science Center [KAW 2018.0452]; European Unions Horizon 2020 research and innovation programme (FET-OPEN-HyPhOE) [800926]; Swedish Research Council [VR-2017-04910, VR 2022-03507]; Swedish Foundation for Strategic Research [ID17-0097, FFL-18-0101]; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]

We present a deep learning model for image segmentation that uses weakly supervised inputs consisting of scribbles. A user can draw scribbles on an image with a brush tool corresponding to the labels they want segmented. The network can segment images in real time while scribbles are being drawn, giving instant feedback to the user. It is easy to correct mistakes made by the network, as more scribbles can be added. During training we use a similar psuedo-interactive and iterative setup to make sure that the network is optimized towards the human-in-the-loop inference setting. On the contrary, standard scribble segmentation methods do not consider the training of the algorithm as an interactive setting and thus are not suited for interactive inference. Our model is class-agnostic and we are able to generalize across many different data modalities. We compare our model with other weakly supervised methods such as bounding box and extrema point methods, and we show our model achieves a better mean DICE score.

Anisotropy reveals interesting details of the subsurface structure of a material. We aim at noninvasive assessment of material anisotropy using as few measurements as possible. To this end, we evaluate different methods for detecting anisotropy when observing (1) several sample rotations, (2) two perpendicular planes of incidence, and (3) just one observation. We estimate anisotropy by fitting ellipses to diffuse reflectance isocontours, and we assess the robustness of this method as we reduce the number of observations. In addition, to support the validity of our ellipse fitting method, we propose a machine learning model for estimating material anisotropy

Crystallisation fouling is a challenge in numerous applications. To mitigate fouling we need to determine the basic mechanisms involved in the process. While ex situ techniques have been widely used in previous studies, they cannot capture complex dynamic effects. We conducted an in situ investigation of the dynamic effects of crystallisation fouling in a steel pipe (length 3 cm, diameter 3 mm) using X-ray micro-computed tomography (µCT) over more than six days. We employed a custom-developed image reconstruction algorithm, which ensured high spatiotemporal resolution from a laboratory instrument. We quantified the evolving fouling using advanced segmentation techniques of 4D images. To understand how the pipe interface structure impacts reactive transport, the experimental geometries of the flow system were used to perform computational fluid dynamic simulations. These new data allowed us to identify three growth phases: initiation, expansion, and densification

Monocular 3D human pose estimation has reached an impressive performance. State-of-the-art models predict joint locations that can be accurately reprojected back into the image, resulting in visually convincing detections. However, our aim is to use the predicted poses in a domain with high- frequency movements, that is, for video of athletes performing golf swings. Our investigation is based on accurate marker-based motion capture data. Also, for our data, the predicted 3D joint locations look convincing when we reproject them into the image. However, by quantitatively com- paring the results with the motion capture data, we see significant model errors that are too erroneous to be used for any kinematic analysis of the movements. Thus we conclude that the current models cannot be used out of the box for advanced golf analytics.

AIMS: Little is known about the mortality for patients with infective endocarditis (IE) on a nationwide scale, and previous studies have been conducted in selected cohorts from tertiary centers. We aimed to investigate temporal trends in mortality using nationwide Danish registries. METHODS AND RESULTS: We identified patients with first-time IE between 1999-2018, and they were grouped by calendar periods (1999-2003, 2004-2008, 2009-2013, 2014-2018). One-year mortality was estimated using Kaplan-Meier estimates. For calendar periods, odds ratios (ORs) and hazard ratios (HRs) were computed using multivariable adjusted logistic regression and Cox proportional Hazards analyses for in-hospital and one-year mortality, respectively. We identified 8804 patients with IE. Age and proportions of men were: 66.7 (25th-75th percentile: 53.4-76.7) years and 59.9% in 1999-2003 and 72.8 (25th-75th percentile: 63.4-80.3) and 65.8% in 2014-2018. In-hospital mortality was 1999-2003: 24.5%, 2004-2008: 22.8%, 2009-2013: 18.8%, and 2014-2018: 18.3%. Relative to 1999-2003, adjusted likelihoods of in-hospital mortality were: OR = 0.81 (95% CI: 0.69-0.96) in 2004-2008, OR = 0.59 (95% CI: 0.50-0.69) in 2009-2013, and OR = 0.51 (95% CI: 0.43-0.60) in 2014-2018. By calendar periods, crude risks of one-year mortality were: 34.4% (95% CI: 32.0-36.8%), 33.5% (95% CI: 31.5-35.6%), 32.1% (95% CI: 30.2-34.0%), and 33.1% (95% CI: 31.3-34.8%). Relative to 1999-2003, adjusted rates of one-year mortality were: HR = 0.88 (95% CI 0.79-0.99) in 2004-2008, HR = 0.76 (95% CI: 0.68-0.86) in 2009-2013, and HR = 0.72 (95% CI: 0.64-0.81) in 2014-2018. CONCLUSION: In this nationwide study of patients with first-time IE between 1999-2018, both short- and long-term survival has improved over time when accounting for changes in patient characteristics. ONE-SENTENCE SUMMARY: When accounting for patient characteristics, both short- and long-term mortality have improved in patients with first-time infective endocardi

Domain shift significantly influences the performance of deep learning algorithms, particularly for object detection within volumetric 3D images. Annotated training data is essential for deep learning-based object detection. However, annotating densely packed objects is time-consuming and costly. Instead, we suggest training models on individually scanned objects, causing a domain shift between training and detection data. To address this challenge, we introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types and 388 volumes of tightly packed bug mixtures. This dataset is characterized by having objects with the same appearance in the source and target domain, which is uncommon for other benchmark datasets for domain shift. During training, individual bug volumes labeled by class are utilized, while testing employs mixtures with center point annotations and bug type labels. Together with the dataset, we provide a baseline detection analysis, aiming at advancing the field of 3D object detection methods., Comment: 20 pages, 6 figures, 2 tables

Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements. With more than 176,000 3D poses from 24 different subjects performing 5 different sports activities, SportsPose provides a diverse and comprehensive set of 3D poses that reflect the complex and dynamic nature of sports movements. Contrary to other markerless datasets we have quantitatively evaluated the precision of SportsPose by comparing our poses with a commercial marker-based system and achieve a mean error of 34.5 mm across all evaluation sequences. This is comparable to the error reported on the commonly used 3DPW dataset. We further introduce a new metric, local movement, which describes the movement of the wrist and ankle joints in relation to the body. With this, we show that SportsPose contains more movement than the Human3.6M and 3DPW datasets in these extremum joints, indicating that our movements are more dynamic. The dataset with accompanying code can be downloaded from our website. We hope that SportsPose will allow researchers and practitioners to develop and evaluate more effective models for the analysis of sports performance and injury prevention. With its realistic and diverse dataset, SportsPose provides a valuable resource for advancing the state-of-the-art in pose estimation in sports.

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

Conducting cellulose composites are promising sustainable functional materials that have found application in energy devices, sensing and water purification. Herein, conducting aerogels are fabricated based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, using the ice templating technique, and their bulk morphology is characterized with X-ray microtomography. The freezing method (-20 degrees C in a freezer vs liquid nitrogen) does not impact the mean porosity of the aerogels but the liquid-N2 aerogels have smaller pores. The integration of carbon fibers as addressing electrodes prior to freezing results in increased mean porosity and pore size in the liquid-N2 aerogels signifying that the carbon fibers alter the morphology of the aerogels when the freezing is fast. Spatially resolved porosity and pore size distributions also reveal that the liquid-N2 aerogels are more inhomogeneous. Independent of the freezing method, the aerogels have similar electrochemical properties. For aerogels without carbon fibers, freezer-aerogels have higher compression modulus and are less stable under cycling compression fatigue test. This can be explained by higher porosity with larger pores in the center of liquid-N2 aerogels and thinner pore walls. This work demonstrates that micro-CT is a powerful tool for characterizing the morphology of aerogels in a non-destructive and spatially resolved manner. Conducting aerogels based on nanofibrillated cellulose and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate are fabricated with the ice templating technique and their bulk morphology is characterized in a spatially resolved manner with X-ray microtomography. The effect of the freezing temperature and the integration of carbon fibers electrodes prior to freezing on the morphology, mechanical, and electrochemical properties is examined., Funding Agencies|Wallenberg Wood Science Center [KAW 2018.0452]; European Unions Horizon 2020 research and innovation programme (FET-OPEN-HyPhOE) [800926]; Swedish Research Council [VR-2017-04910, VR 2022-03507]; Swedish Foundation for Strategic Research [ID17-0097, FFL-18-0101]; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.

Purpose Streptococcal bloodstream infections (BSIs) are common, yet prognostic factors are poorly investigated. We aimed to investigate the mortality according to streptococcal species and seasonal variation. Methods Patients with streptococcal BSIs from 2008 to 2017 in the Capital Region of Denmark were investigated, and data were crosslinked with nationwide registers for the identification of comorbidities. A multivariable logistic regression analysis was performed to assess mortality according to streptococcal species and season of infection. Results Among 6095 patients with a streptococcal BSI (mean age 68.1 years), the 30-day mortality was 16.1% and the one-year mortality was 31.5%. With S. pneumoniae as a reference, S. vestibularis was associated with a higher adjusted mortality both within 30 days (odds ratio (OR) 2.89 [95% confidence interval (CI) 1.20–6.95]) and one year (OR 4.09 [95% CI 1.70–9.48]). One-year mortality was also higher in S. thermophilus, S. constellatus, S. parasanguinis, S. salivarius, S. anginosus, and S. mitis/oralis. However, S. mutans was associated with a lower one-year mortality OR 0.44 [95% CI 0.20–0.97], while S. gallolyticus was associated with both a lower 30-day (OR 0.42 [95% CI 0.26–0.67]) and one-year mortality (OR 0.66 [95% CI 0.48–0.93]). Furthermore, with infection in the summer as a reference, patients infected in the winter and autumn had a higher association with 30-day mortality., Purpose: Streptococcal bloodstream infections (BSIs) are common, yet prognostic factors are poorly investigated. We aimed to investigate the mortality according to streptococcal species and seasonal variation. Methods: Patients with streptococcal BSIs from 2008 to 2017 in the Capital Region of Denmark were investigated, and data were crosslinked with nationwide registers for the identification of comorbidities. A multivariable logistic regression analysis was performed to assess mortality according to streptococcal species and season of infection. Results: Among 6095 patients with a streptococcal BSI (mean age 68.1 years), the 30-day mortality was 16.1% and the one-year mortality was 31.5%. With S. pneumoniae as a reference, S. vestibularis was associated with a higher adjusted mortality both within 30 days (odds ratio (OR) 2.89 [95% confidence interval (CI) 1.20–6.95]) and one year (OR 4.09 [95% CI 1.70–9.48]). One-year mortality was also higher in S. thermophilus, S. constellatus, S. parasanguinis, S. salivarius, S. anginosus, and S. mitis/oralis. However, S. mutans was associated with a lower one-year mortality OR 0.44 [95% CI 0.20–0.97], while S. gallolyticus was associated with both a lower 30-day (OR 0.42 [95% CI 0.26–0.67]) and one-year mortality (OR 0.66 [95% CI 0.48–0.93]). Furthermore, with infection in the summer as a reference, patients infected in the winter and autumn had a higher association with 30-day mortality. Conclusions: The mortality in patients with streptococcal BSI was associated with streptococcal species. Further, patients with streptococcal BSIs infected in the autumn and winter had a higher risk of death within 30 days, compared with patients infected in the summer.

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

Purpose: Infective endocarditis (IE) is frequently caused by streptococcal species, yet clinical features and mortality are poorly investigated. Our aim was to examine patients with streptococcal IE to describe clinical features and outcomes according to streptococcal species. Methods: From 2002 to 2012, we investigated patients with IE admitted to two tertiary Danish heart centres. Adult patients with left-sided streptococcal IE were included. Adjusted multivariable logistic regression analyses were performed, to assess the association between streptococcal species and heart valve surgery or 1-year mortality. Results: Among 915 patients with IE, 284 (31%) patients with streptococcal IE were included [mean age 63.5 years (SD 14.1), 69% men]. The most frequent species were S. mitis/oralis (21%) and S. gallolyticus (17%). Fever (86%) and heart murmur (81%) were common symptoms, while dyspnoea was observed in 46%. Further, 18% of all cases were complicated by a cardiac abscess/pseudoaneurysm and 25% by an embolic event. Heart valve surgery during admission was performed in 55% of all patients, and S. gallolyticus (OR 0.28 [95% CI 0.11–0.69]) was associated with less surgery compared with S. mitis/oralis. In-hospital mortality was 7% and 1-year mortality 15%, without any difference between species. Conclusion: S. mitis/oralis and S. gallolyticus were the most frequent streptococcal species causing IE. Further, S. gallolyticus IE was associated with less heart valve surgery during admission compared with S. mitis/oralis IE. Being aware of specific symptoms, clinical findings, and complications related to different streptococcal species, may help the clinicians in expecting different outcomes.

Deducing a 3D human pose from a single 2D image or 2D keypoints is inherently challenging, given the fundamental ambiguity wherein multiple 3D poses can correspond to the same 2D representation. The acquisition of 3D data, while invaluable for resolving pose ambiguity, is expensive and requires an intricate setup, often restricting its applicability to controlled lab environments. We improve performance of monocular human pose estimation models using multiview data for fine-tuning. We propose a novel loss function, multiview consistency, to enable adding additional training data with only 2D supervision. This loss enforces that the inferred 3D pose from one view aligns with the inferred 3D pose from another view under similarity transformations. Our consistency loss substantially improves performance for fine-tuning with no available 3D data. Our experiments demonstrate that two views offset by 90 degrees are enough to obtain good performance, with only marginal improvements by adding more views. Thus, we enable the acquisition of domain-specific data by capturing activities with off-the-shelf cameras, eliminating the need for elaborate calibration procedures. This research introduces new possibilities for domain adaptation in 3D pose estimation, providing a practical and cost-effective solution to customize models for specific applications. The used dataset, featuring additional views, will be made publicly available.

Background: Understanding the diversity of eyes is crucial to unravel how different animals use vision to interact with their respective environments. To date, comparative studies of eye anatomy are scarce because they often involve time-consuming or inefficient methods. X-ray micro-tomography (micro-CT) is a promising high-throughput imaging technique that enables to reconstruct the 3D anatomy of eyes, but powerful tools are needed to perform fast conversions of anatomical reconstructions into functional eye models. Results: We developed a computing method named InSegtCone to automatically segment the crystalline cones in the apposition compound eyes of arthropods. Here, we describe the full auto-segmentation process, showcase its application to three different insect compound eyes and evaluate its performance. The auto-segmentation could successfully label the full individual shapes of 60-80% of the crystalline cones and is about as accurate and 250 times faster than manual labelling of the individual cones. Conclusions: We believe that InSegtCone can be an important tool for peer scientists to measure the orientation, size and dynamics of crystalline cones, leading to the accurate optical modelling of the diversity of arthropod eyes with micro-CT.

Background Staphylococcus aureus bacteremia (SAB) can be community-acquired or healthcare-associated, and prior small studies have suggested that this mode of acquisition impacts the subsequent prevalence of infective endocarditis (IE) and patient outcomes. Methods First-time SAB was identified from 2010 to 2018 using Danish nationwide registries and categorized into community-acquired (no healthcare contact within 30 days) or healthcare-associated (SAB >48 hours of hospital admission, hospitalization within 30 days, or outpatient hemodialysis). Prevalence of IE (defined from hospital codes) was compared between groups using multivariable adjusted logistic regression analysis. One-year mortality of S aureus IE (SAIE) was compared between groups using multivariable adjusted Cox proportional hazard analysis. Results We identified 5549 patients with community-acquired SAB and 7491 with healthcare-associated SAB. The prevalence of IE was 12.1% for community-acquired and 6.6% for healthcare-associated SAB. Community-acquired SAB was associated with a higher odds of IE as compared with healthcare-associated SAB (odds ratio, 2.12 [95% confidence interval {CI}, 1.86–2.41]). No difference in mortality was observed with 0–40 days of follow-up for community-acquired SAIE as compared with healthcare-associated SAIE (HR, 1.07 [95% CI, .83–1.37]), while with 41–365 days of follow-up, community-acquired SAIE was associated with a lower mortality (HR, 0.71 [95% CI, .53–.95]). Conclusions Community-acquired SAB was associated with twice the odds for IE, as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community-acquired and healthcare-associated SAIE. Beyond 40 days of survival, community-acquired SAIE was associated with a lower mortality., Community-acquired Staphylococcus aureus bacteremia (SAB) was associated with twice the odds for infective endocarditis (IE), as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community-acquired and healthcare-associated S aureus IE.Background Staphylococcus aureus bacteremia (SAB) can be community-acquired or healthcare-associated, and prior small studies have suggested that this mode of acquisition impacts the subsequent prevalence of infective endocarditis (IE) and patient outcomes. Methods First-time SAB was identified from 2010 to 2018 using Danish nationwide registries and categorized into community-acquired (no healthcare contact within 30 days) or healthcare-associated (SAB >48 hours of hospital admission, hospitalization within 30 days, or outpatient hemodialysis). Prevalence of IE (defined from hospital codes) was compared between groups using multivariable adjusted logistic regression analysis. One-year mortality of S aureus IE (SAIE) was compared between groups using multivariable adjusted Cox proportional hazard analysis. Results We identified 5549 patients with community-acquired SAB and 7491 with healthcare-associated SAB. The prevalence of IE was 12.1% for community-acquired and 6.6% for healthcare-associated SAB. Community-acquired SAB was associated with a higher odds of IE as compared with healthcare-associated SAB (odds ratio, 2.12 [95% confidence interval {CI}, 1.86-2.41]). No difference in mortality was observed with 0-40 days of follow-up for community-acquired SAIE as compared with healthcare-associated SAIE (HR, 1.07 [95% CI, .83-1.37]), while with 41-365 days of follow-up, community-acquired SAIE was associated with a lower mortality (HR, 0.71 [95% CI, .53-.95]). Conclusions Community-acquired SAB was associated with twice the odds for IE, as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community

We address a subclass of segmentation problems where the labels of the image are structured in layers. We propose applying autoregressive CNNs which, when given an image and a partial segmentation of layers, complete the segmentation. Initializing the model with a user-provided partial segmentation allows for choosing which layers the model should segment. Alternatively, the model can produce an automatic initialization, albeit with some performance loss. The model is trained exclusively on synthetic data from our data generation algorithm. It yields impressive performance on the synthetic data and generalizes to real data it has never seen.

In many clinical applications, 3D mesh models of human anatomies are important tools for visualization, diagnosis, and treatment planning. Such 3D mesh models often have a high number of vertices to capture the complex shape, and processing these large meshes on readily available graphic cards can be a challenging task. To accommodate this, we present a sparse version of MeshCNN called SparseMeshCNN, which can process meshes with more than 60 000 edges. We further show that adding non-local attention in the network can mitigate the small receptive field and improve the results. The developed methodology was applied to separate the Left Atrial Appendage (LAA) from the Left Atrium (LA) on 3D mesh models constructed from medical images, but the method is general and can be put to use in any application within mesh classification or segmentation where memory can be a concern.

Background Staphylococcus aureus bacteremia (SAB) can be community-acquired or healthcare-associated, and prior small studies have suggested that this mode of acquisition impacts the subsequent prevalence of infective endocarditis (IE) and patient outcomes. Methods First-time SAB was identified from 2010 to 2018 using Danish nationwide registries and categorized into community-acquired (no healthcare contact within 30 days) or healthcare-associated (SAB >48 hours of hospital admission, hospitalization within 30 days, or outpatient hemodialysis). Prevalence of IE (defined from hospital codes) was compared between groups using multivariable adjusted logistic regression analysis. One-year mortality of S aureus IE (SAIE) was compared between groups using multivariable adjusted Cox proportional hazard analysis. Results We identified 5549 patients with community-acquired SAB and 7491 with healthcare-associated SAB. The prevalence of IE was 12.1% for community-acquired and 6.6% for healthcare-associated SAB. Community-acquired SAB was associated with a higher odds of IE as compared with healthcare-associated SAB (odds ratio, 2.12 [95% confidence interval {CI}, 1.86–2.41]). No difference in mortality was observed with 0–40 days of follow-up for community-acquired SAIE as compared with healthcare-associated SAIE (HR, 1.07 [95% CI, .83–1.37]), while with 41–365 days of follow-up, community-acquired SAIE was associated with a lower mortality (HR, 0.71 [95% CI, .53–.95]). Conclusions Community-acquired SAB was associated with twice the odds for IE, as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community-acquired and healthcare-associated SAIE. Beyond 40 days of survival, community-acquired SAIE was associated with a lower mortality., Community-acquired Staphylococcus aureus bacteremia (SAB) was associated with twice the odds for infective endocarditis (IE), as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community-acquired and healthcare-associated S aureus IE.Background Staphylococcus aureus bacteremia (SAB) can be community-acquired or healthcare-associated, and prior small studies have suggested that this mode of acquisition impacts the subsequent prevalence of infective endocarditis (IE) and patient outcomes. Methods First-time SAB was identified from 2010 to 2018 using Danish nationwide registries and categorized into community-acquired (no healthcare contact within 30 days) or healthcare-associated (SAB >48 hours of hospital admission, hospitalization within 30 days, or outpatient hemodialysis). Prevalence of IE (defined from hospital codes) was compared between groups using multivariable adjusted logistic regression analysis. One-year mortality of S aureus IE (SAIE) was compared between groups using multivariable adjusted Cox proportional hazard analysis. Results We identified 5549 patients with community-acquired SAB and 7491 with healthcare-associated SAB. The prevalence of IE was 12.1% for community-acquired and 6.6% for healthcare-associated SAB. Community-acquired SAB was associated with a higher odds of IE as compared with healthcare-associated SAB (odds ratio, 2.12 [95% confidence interval {CI}, 1.86-2.41]). No difference in mortality was observed with 0-40 days of follow-up for community-acquired SAIE as compared with healthcare-associated SAIE (HR, 1.07 [95% CI, .83-1.37]), while with 41-365 days of follow-up, community-acquired SAIE was associated with a lower mortality (HR, 0.71 [95% CI, .53-.95]). Conclusions Community-acquired SAB was associated with twice the odds for IE, as compared with healthcare-associated SAB. We identified no significant difference in short-term mortality between community

The modified Duke criteria requires that Enterococcus faecalis bacteremia must be both community-acquired and without known focus in order to be considered a microbiological "Major" diagnostic criterion in the diagnosis of infective endocarditis. We believe that the microbiological diagnostic criteria should be updated to regard E. faecalis as a "typical" endocarditis bacterium as is currently the case, for example, viridans group streptococci and Staphylococcus aureus. Using data from a prospective study of 344 patients with E. faecalis bacteremia evaluated with echocardiography, we demonstrate that designating E. faecalis as a "typical" endocarditis pathogen, regardless the place of acquisition or the portal of entry, improved the sensitivity to correctly identify definite endocarditis from 70% (modified Duke criteria) to 96% (enterococcal adjusted Duke criteria).

To study the shape of objects using geometric morphometrics, landmarks are oftentimes collected digitally from a 3D scanned model. The expert may annotate landmarks using software that visualizes the 3D model on a flat screen, and interaction is achieved with a mouse and a keyboard. However, landmark annotation of a 3D model on a 2D display is a tedious process and potentially introduces error due to the perception and interaction limitations of the flat interface. In addition, digital landmark placement can be more time-consuming than direct annotation on the physical object using a tactile digitizer arm. Since virtual reality (VR) is designed to more closely resemble the real world, we present a VR prototype for annotating landmarks on 3D models. We study the impact of VR on annotation performance by comparing our VR prototype to Stratovan Checkpoint, a commonly used commercial desktop software. We use an experimental setup, where four operators placed six landmarks on six grey seal (Halichoerus grypus) skulls in six trials for both systems. This enables us to investigate multiple sources of measurement error. We analyse both for the configuration and for single landmarks. Our analysis shows that annotation in VR is a promising alternative to desktop annotation. We find that annotation precision is comparable between the two systems, with VR being significantly more precise for one of the landmarks. We do not find evidence that annotation in VR is faster than on the desktop, but it is accurate.

To study the shape of objects using geometric morphometrics, landmarks are oftentimes collected digitally from a 3D scanned model. The expert may annotate landmarks using software that visualizes the 3D model on a flat screen, and interaction is achieved with a mouse and a keyboard. However, landmark annotation of a 3D model on a 2D display is a tedious process and potentially introduces error due to the perception and interaction limitations of the flat interface. In addition, digital landmark placement can be more time-consuming than direct annotation on the physical object using a tactile digitizer arm. Since virtual reality (VR) is designed to more closely resemble the real world, we present a VR prototype for annotating landmarks on 3D models. We study the impact of VR on annotation performance by comparing our VR prototype to Stratovan Checkpoint, a commonly used commercial desktop software. We use an experimental setup, where four operators placed six landmarks on six grey seal (Halichoerus grypus) skulls in six trials for both systems. This enables us to investigate multiple sources of measurement error. We analyse both for the configuration and for single landmarks. Our analysis shows that annotation in VR is a promising alternative to desktop annotation. We find that annotation precision is comparable between the two systems, with VR being significantly more precise for one of the landmarks. We do not find evidence that annotation in VR is faster than on the desktop, but it is accurate.

Super resolution (SR) imaging is currently conducted using fragile ultrasound contrast agents. This precludes using the full acoustic pressure range, and the distribution of bubbles has to be sparse for them to be isolated for SR imaging. Images have to be acquired over minutes to accumulate enough positions for visualizing the vas- culature. A new method for SUper Resolution imaging using the Erythrocytes (SURE) as targets is introduced, which makes it possible to maximize the emitted pressure for good signal-to-noise ratios. The abundant number of erythrocyte targets make acquisition fast, and the SURE images can be acquired in seconds. A Verasonics Vantage 256 scanner was used in combination with a GE L8-18iD linear array probe operated at 10 MHz for a wavelength of 150 m. A 12 emissions synthetic aperture ultrasound sequence was employed to scan the kidney of a Sprague-Dawley rat for 24 seconds to visualize its vasculature. An ex vivo micro-CT image using the contrast agent Microfil was also acquired at a voxel size of 22.6 m for validating the SURE images. The SURE image revealed vessels with a size down to 29 fim, five times smaller than the ultrasound wavelength, and the dense grid of vessels in the full kidney was reliably shown for scan times between 1 to 24 seconds. Visually the SURE images revealed the same vasculature as the micro-CT images. SURE images are acquired in seconds rather than minutes without contrast injection for easy clinical use, and they can be measured at full regulatory levels for pressure, intensity, and probe temperature.

The large interspecific variation in marine mammal skull and dental morphology reflects ecological specialisations to foraging and communication. At the intraspecific level, the drivers of skull shape variation are less well understood, having implications for identifying putative local foraging adaptations and delineating populations and subspecies for taxonomy, systematics, management and conservation. Here, we assess the range-wide intraspecific variation in 71 grey seal skulls by 3D surface scanning, collection of cranial landmarks and geometric morphometric analysis. We find that skull shape differs slightly between populations in the Northwest Atlantic, Northeast Atlantic and Baltic Sea. However, there was a large shape overlap between populations and variation was substantially larger among animals within populations than between. We hypothesize that this pattern of intraspecific variation in grey seal skull shape results from balancing selection or phenotypic plasticity allowing for a remarkably generalist foraging behaviour. Moreover, the large overlap in skull shape between populations implies that the separate subspecies status of Atlantic and Baltic Sea grey seals is questionable from a morphological point of view.

Current super resolution imaging is conducted using ultrasound contrast agents, where a sparse distribution ofbubbles must be employed to separate individual targets. Thesparse targets make the acquisition time long in the range of1 to 10 minutes, and therefore demands an accurate motioncorrection over a long time. The employment of a contrastagent also lowers MI to below 0.2 to not disrupt the bubbles,with a corresponding lower signal-to-noise ratio in the images.A new method, SURE (SUper Resolution ultrasound imagingusing Erythrocytes), where erythrocytes are used as targets, issuggested to alleviate these problems. Perfused tissues containan abundance of targets, and the full clinical pressure rangecan be used. It is hypothesized that super resolution imagingbelow the diffraction limit can be attained in seconds usingSURE imaging. A SURE processing pipeline was developed withmodules for beamforming, tissue motion estimation, alignment,singular value decomposition for echo canceling, and subsequentpeak detection in the speckle pattern. The detected peaks weresummed in a high-resolution image for yielding the SURE image.Data were acquired using a 10 MHz linear array GE L10-18i probe (150 µm wavelength) and a Verasonics Vantage 256scanner. A synthetic aperture scan sequence with 12 emissionswas employed at a pulse repetition frequency of 5 kHz for a 417Hz frame rate. Kidneys of Sprague-Dawley rats were scanned for24 seconds and RF data stored for off-line processing. The excisedkidneys were micro-CT scanned for 11 hours for generatingreference maps of the vasculature with a voxel size of 21 µm.SURE images revealed vessels with sizes down to 50 µm. Fourierring correlations between independent images measured for 12s revealed a resolution between 25 to 49 µm, demonstrating thesuper resolution capability of the method. The SURE images areobtained in 1 to 12 seconds, demand no injection of intravenouscontrast agents, and can use the full pressure and intensityrange allo

The large interspecific variation in marine mammal skull and dental morphology reflects ecological specialisations to foraging and communication. At the intraspecific level, the drivers of skull shape variation are less well understood, having implications for identifying putative local foraging adaptations and delineating populations and subspecies for taxonomy, systematics, management and conservation. Here, we assess the range-wide intraspecific variation in 71 grey seal skulls by 3D surface scanning, collection of cranial landmarks and geometric morphometric analysis. We find that skull shape differs slightly between populations in the Northwest Atlantic, Northeast Atlantic and Baltic Sea. However, there was a large shape overlap between populations and variation was substantially larger among animals within populations than between. We hypothesize that this pattern of intraspecific variation in grey seal skull shape results from balancing selection or phenotypic plasticity allowing for a remarkably generalist foraging behaviour. Moreover, the large overlap in skull shape between populations implies that the separate subspecies status of Atlantic and Baltic Sea grey seals is questionable from a morphological point of view.

Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.

The large interspecific variation in marine mammal skull and dental morphology reflects ecological specialisations to foraging and communication. At the intraspecific level, the drivers of skull shape variation are less well understood, having implications for identifying putative local foraging adaptations and delineating populations and subspecies for taxonomy, systematics, management and conservation. Here, we assess the range-wide intraspecific variation in 71 grey seal skulls by 3D surface scanning, collection of cranial landmarks and geometric morphometric analysis. We find that skull shape differs slightly between populations in the Northwest Atlantic, Northeast Atlantic and Baltic Sea. However, there was a large shape overlap between populations and variation was substantially larger among animals within populations than between. We hypothesize that this pattern of intraspecific variation in grey seal skull shape results from balancing selection or phenotypic plasticity allowing for a remarkably generalist foraging behaviour. Moreover, the large overlap in skull shape between populations implies that the separate subspecies status of Atlantic and Baltic Sea grey seals is questionable from a morphological point of view.

Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.

Background Elderly individuals occupy an increasing part of the general population. Conventional and speckle-tracking transthoracic echocardiography may help guide risk stratification in these individuals. The purpose of this study was to evaluate the potential utility of conventional and speckle-tracking echocardiography in the screening of cardiac abnormalities in the elderly population. Methods Two cohorts of elderly individuals (sample size: 1441 and 944) were analyzed, who were part of a randomized controlled clinical trial (LOOP study) and of an observational study (Copenhagen City Heart Study), recruiting participants from the general population >70 years of age with cardiovascular risk factors (arterial hypertension, diabetes mellitus, heart failure, or prior stroke) and sinus rhythm. Participants underwent a comprehensive transthoracic echocardiographic examination, including myocardial speckle tracking. Cardiac abnormalities were defined according to the ASE/EACVI guidelines. Results Structural cardiac abnormalities such as left ventricular (LV) remodeling, mitral annular calcification (MAC), and aortic valve sclerosis (with or without stenosis) were highly prevalent in the LOOP study (40%, 39%, and 27%, respectively). Moreover, a high prevalence of functional cardiac alterations such as LV diastolic dysfunction (LVDD), abnormal LV longitudinal systolic strain (GLS), and abnormal left atrial (LA) reservoir strain was present in the LOOP study (27%, 18%, and 9%, respectively). Likewise, the rate of LVDD, abnormal GLS, and abnormal LA reservoir strain was comparable in the validation sample from the Copenhagen City Heart Study. In line with these findings, subjects with LV remodeling, MAC, and aortic valve changes had a higher prevalence of LVDD, abnormal GLS, and abnormal LA reservoir strain than those without structural cardiac alterations. Conclusion The findings of this study highlight the potential clinical utility of conventional and speckle-trac

Super resolution (SR) imaging is currently conducted using fragile ultrasound contrast agents. This precludes using the full acoustic pressure range, and the distribution of bubbles has to be sparse for them to be isolated for SR imaging. Images have to be acquired over minutes to accumulate enough positions for visualizing the vas- culature. A new method for SUper Resolution imaging using the Erythrocytes (SURE) as targets is introduced, which makes it possible to maximize the emitted pressure for good signal-to-noise ratios. The abundant number of erythrocyte targets make acquisition fast, and the SURE images can be acquired in seconds. A Verasonics Vantage 256 scanner was used in combination with a GE L8-18iD linear array probe operated at 10 MHz for a wavelength of 150 m. A 12 emissions synthetic aperture ultrasound sequence was employed to scan the kidney of a Sprague-Dawley rat for 24 seconds to visualize its vasculature. An ex vivo micro-CT image using the contrast agent Microfil was also acquired at a voxel size of 22.6 m for validating the SURE images. The SURE image revealed vessels with a size down to 29 fim, five times smaller than the ultrasound wavelength, and the dense grid of vessels in the full kidney was reliably shown for scan times between 1 to 24 seconds. Visually the SURE images revealed the same vasculature as the micro-CT images. SURE images are acquired in seconds rather than minutes without contrast injection for easy clinical use, and they can be measured at full regulatory levels for pressure, intensity, and probe temperature.

Four-dimensional (4D) echocardiography may provide more accurate estimations of left atrial (LA) volumes than 2-dimensional (2D) measures. We sought to compare the concordance of a novel 4D LA quantification software versus 2D echocardiography against cardiac magnetic resonance (CMR). This was a multimodality imaging substudy of a randomized clinical trial (the LOOP study). Elderly participants with stroke risk factors were included. A subgroup of this study population underwent transthoracic echocardiography (n = 1441) and a subset underwent CMR within two weeks (n = 73). The mean age of the echocardiographic study population was 74 years and 54% were men. The maximal LA volume (LAVmax) was 47 mL by 2D, 52 mL by 4D, and 104 mL by CMR. While 2D echocardiography showed a moderate correlation with 4D (R2 = 0.51) it yielded significantly lower values for LAVmax with a mean difference of 4.5 ± 11.9 mL, p < 0.001. 4D echocardiography correlated strongly with CMR measurements (R2 = 0.70), whereas 2D echocardiography showed a moderate correlation (R2 = 0.53). However, both modalities systematically underestimated LAVmax largely compared to CMR (2D vs. CMR: − 54.9 ± 21.3 mL; 4D vs. CMR: − 49.7 ± 18.6 mL). Similar observations were made for minimal LA volume and LA volume before atrial contraction. Analyses time by 4D was shorter than for 2D (90 ± 11 vs. 118 ± 16 s, p < 0.001). Intra- and interobserver variability was lower for 4D than 2D. Four-dimensional echocardiography is faster, more reproducible, and correlates more closely to CMR than 2D echocardiography. Both 4D and 2D echocardiography systematically underestimates LA volumes compared to CMR, emphasizing that values of LA volumes are not interchangeable between echocardiography and CMR.

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research., Comment: 20 pages, 13 figures, accepted for publication at T-PAMI

Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.