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| 2. |
- Groß, M., et al.
(författare)
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Rubella vaccine–induced granulomas are a novel phenotype with incomplete penetrance of genetic defects in cytotoxicity
- 2022
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Ingår i: Journal of Allergy and Clinical Immunology. - : Elsevier BV. - 0091-6749 .- 1097-6825. ; 149:1, s. 388-399
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Tidskriftsartikel (refereegranskat)abstract
- Background: Rubella virus–induced granulomas have been described in patients with various inborn errors of immunity. Most defects impair T-cell immunity, suggesting a critical role of T cells in rubella elimination. However, the molecular mechanism of virus control remains elusive. Objective: This study sought to understand the defective effector mechanism allowing rubella vaccine virus persistence in granulomas. Methods: Starting from an index case with Griscelli syndrome type 2 and rubella skin granulomas, this study combined an international survey with a literature search to identify patients with cytotoxicity defects and granuloma. The investigators performed rubella virus immunohistochemistry and PCR and T-cell migration assays. Results: This study identified 21 patients with various genetically confirmed cytotoxicity defects, who presented with skin and visceral granulomas. Rubella virus was demonstrated in all 12 accessible biopsies. Granuloma onset was typically before 2 years of age and lesions persisted from months to years. Granulomas were particularly frequent in MUNC13-4 and RAB27A deficiency, where 50% of patients at risk were affected. Although these proteins have also been implicated in lymphocyte migration, 3-dimensional migration assays revealed no evidence of impaired migration of patient T cells. Notably, patients showed no evidence of reduced control of concomitantly given measles, mumps, or varicella live-attenuated vaccine or severe infections with other viruses. Conclusions: This study identified lymphocyte cytotoxicity as a key effector mechanism for control of rubella vaccine virus, without evidence for its need in control of live measles, mumps, or varicella vaccines. Rubella vaccine–induced granulomas are a novel phenotype with incomplete penetrance of genetic disorders of cytotoxicity. © 2021 American Academy of Allergy, Asthma & Immunology
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| 3. |
- Maier, O., et al.
(författare)
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ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
- 2017
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 35, s. 250-269
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Tidskriftsartikel (refereegranskat)abstract
- Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
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| 4. |
- Heckemann, Rolf A., et al.
(författare)
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Brain Extraction Using Label Propagation and Group Agreement: Pincram
- 2015
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Ingår i: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the " success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.
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| 5. |
- Ledig, C., et al.
(författare)
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Robust whole-brain segmentation: Application to traumatic brain injury
- 2015
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 21:1, s. 40-58
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Tidskriftsartikel (refereegranskat)abstract
- We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas-Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
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| 6. |
- Ledig, C., et al.
(författare)
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Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database
- 2018
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
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