| 1. |
- Boyer, E, et al.
(författare)
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SHREC 2011: Robust Feature Detection and Description Benchmark
- 2011
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Konferensbidrag (refereegranskat)abstract
- Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC’11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC’11 robust feature detection and description benchmark results.
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| 2. |
- Buehler, Alexandra, et al.
(författare)
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cNGR: A novel homing sequence for CD13/APN targeted molecular imaging of murine cardiac angiogenesis in vivo
- 2006
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Ingår i: Arteriosclerosis, Thrombosis and Vascular Biology. - : American Heart Association. - 1079-5642 .- 1524-4636. ; 26:12, s. 2681-2687
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE:Previously, the peptide sequence cNGR has been shown to home specifically to CD13/APN (aminopeptidase N) on tumor endothelium. Here, we investigated the feasibility of selective imaging of cardiac angiogenesis using the cNGR-CD13/APN system.METHODS AND RESULTS:CD13/APN induction and cNGR homing were studied in the murine myocardial infarction (MI) model. By real-time polymerase chain reaction (PCR) at 7 days after MI, CD13/APN expression was 10- to 20-fold higher in the angiogenic infarct border zone and the MI area than in non-MI areas. In vivo fluorescence microscopy confirmed specific homing of fluorophore-tagged cNGR to the border zone and MI territory at 4 and 7 days after MI with a local advantage of 2.3, but not at 1 or 14 days after MI. Tissue residence half-life was 9.1+/-0.3 hours, whereas the half-life in plasma was 15.4+/-3.4 minutes. Pulse chase experiments confirmed reversible binding of cNGR in the infarct area. Fluorescent labeled cNGR conjugates or antibodies were injected in vivo, and their distribution was studied ex vivo by 2-photon laser scanning microscopy (TPLSM). cNGR co-localized exclusively with CD13/APN and the endothelial marker CD31 on vessels.CONCLUSIONS:In cardiac angiogenesis endothelial CD13/APN is upregulated. It can be targeted specifically with cNGR conjugates. In the heart cNGR binds its endothelial target only in angiogenic areas.
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| 3. |
- Hermsen, Meyke, et al.
(författare)
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Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies
- 2022
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Ingår i: American Journal of Pathology. - : ELSEVIER SCIENCE INC. - 0002-9440 .- 1525-2191. ; 192:10, s. 1418-1432
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Tidskriftsartikel (refereegranskat)abstract
- In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3+ cell density within scarred regions and higher CD3+ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies. (Am J Pathol 2022, 192: 1418-1432; https://doi.org/10.1016/j.ajpath.2022.06.009)
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| 4. |
- Hermsen, Meyke, et al.
(författare)
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Deep Learning-Based Histopathologic Assessment of Kidney Tissue
- 2019
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Ingår i: Journal of the American Society of Nephrology. - : AMER SOC NEPHROLOGY. - 1046-6673 .- 1533-3450. ; 30:10, s. 1968-1979
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Tidskriftsartikel (refereegranskat)abstract
- Background The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). Methods We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the networks glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was "glomeruli" in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by "tubuli combined" and "interstitium." The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.
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| 5. |
- Okely, Anthony D., et al.
(författare)
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Cross-sectional examination of 24-hour movement behaviours among 3-and 4-year-old children in urban and rural settings in low-income, middle-income and high-income countries : the SUNRISE study protocol
- 2021
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 11:10
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Tidskriftsartikel (refereegranskat)abstract
- Introduction 24-hour movement behaviours (physical activity, sedentary behaviour and sleep) during the early years are associated with health and developmental outcomes, prompting the WHO to develop Global guidelines for physical activity, sedentary behaviour and sleep for children under 5 years of age. Prevalence data on 24-hour movement behaviours is lacking, particularly in low-income and middle-income countries (LMICs). This paper describes the development of the SUNRISE International Study of Movement Behaviours in the Early Years protocol, designed to address this gap. Methods and analysis SUNRISE is the first international cross-sectional study that aims to determine the proportion of 3- and 4-year-old children who meet the WHO Global guidelines. The study will assess if proportions differ by gender, urban/rural location and/or socioeconomic status. Executive function, motor skills and adiposity will be assessed and potential correlates of 24-hour movement behaviours examined. Pilot research from 24 countries (14 LMICs) informed the study design and protocol. Data are collected locally by research staff from partnering institutions who are trained throughout the research process. Piloting of all measures to determine protocol acceptability and feasibility was interrupted by COVID-19 but is nearing completion. At the time of publication 41 countries are participating in the SUNRISE study. Ethics and dissemination The SUNRISE protocol has received ethics approved from the University of Wollongong, Australia, and in each country by the applicable ethics committees. Approval is also sought from any relevant government departments or organisations. The results will inform global efforts to prevent childhood obesity and ensure young children reach their health and developmental potential. Findings on the correlates of movement behaviours can guide future interventions to improve the movement behaviours in culturally specific ways. Study findings will be disseminated via publications, conference presentations and may contribute to the development of local guidelines and public health interventions.
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