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1.
  • Bott, Lukas Thomas, et al. (creator_code:aut_t)
  • Coulomb dissociation of O-16 into He-4 and C-12
  • 2023
  • record:In_t: NUCLEAR PHYSICS IN ASTROPHYSICS - X, NPA-X 2022. - : EDP Sciences. - 2100-014X. ; 279
  • swepub:Mat_conferencepaper_t (swepub:level_refereed_t)abstract
    • We measured the Coulomb dissociation of O-16 into He-4 and C-12 within the FAIR Phase-0 program at GSI Helmholtzzentrum fur Schwerionenforschung Darmstadt, Germany. From this we will extract the photon dissociation cross section O-16(alpha,gamma)C-12, which is the time reversed reaction to C-12(alpha,gamma)O-16. With this indirect method, we aim to improve on the accuracy of the experimental data at lower energies than measured so far. The expected low cross section for the Coulomb dissociation reaction and close magnetic rigidity of beam and fragments demand a high precision measurement. Hence, new detector systems were built and radical changes to the (RB)-B-3 setup were necessary to cope with the high-intensity O-16 beam. All tracking detectors were designed to let the unreacted O-16 ions pass, while detecting the C-12 and He-4.
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2.
  • Ravikumar, Sadhana, et al. (creator_code:aut_t)
  • Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace’s Equation
  • 2023
  • record:In_t: Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings. - 1611-3349 .- 0302-9743. - 9783031340475 ; 13939 LNCS, s. 692-704
  • swepub:Mat_conferencepaper_t (swepub:level_refereed_t)abstract
    • When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace’s equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.
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3.
  • Ravikumar, Sadhana, et al. (creator_code:aut_t)
  • Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging
  • 2021
  • record:In_t: Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030875855 ; 13001 LNCS, s. 3-12
  • swepub:Mat_conferencepaper_t (swepub:level_refereed_t)abstract
    • Neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s Disease (AD). In this work, we investigate the relationship between MTL morphometry features derived from high-resolution ex vivo imaging and histology-based measures of NFT pathology using a topological unfolding framework applied to a dataset of 18 human postmortem MTL specimens. The MTL has a complex 3D topography and exhibits a high degree of inter-subject variability in cortical folding patterns which poses a significant challenge for volumetric registration methods typically used during MRI template construction. By unfolding the MTL cortex, the proposed framework explicitly accounts for the sheet-like geometry of the MTL cortex and provides a two-dimensional reference coordinate space which can be used to implicitly register cortical folding patterns across specimens based on distance along the cortex despite large anatomical variability. Leveraging this framework in a subset of 15 specimens, we characterize the associations between NFTs and morphological features such as cortical thickness and surface curvature and identify regions in the MTL where patterns of atrophy are strongly correlated with NFT pathology.
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4.
  • Roh, Hyung S., et al. (creator_code:aut_t)
  • Integrating Color Deconvolution Thresholding and Weakly Supervised Learning for Automated Segmentation of Neurofibrillary Tangle and Neuropil Threads
  • 2023
  • record:In_t: Medical Imaging 2023 : Digital and Computational Pathology - Digital and Computational Pathology. - 1605-7422. - 9781510660472 ; 12471
  • swepub:Mat_conferencepaper_t (swepub:level_refereed_t)abstract
    • Abnormally phosphorylated tau proteins are known to be a major indicator of Alzheimer's Disease (AD) with strong association with memory loss and cognitive decline. Automated generation of pixel-wise accurate neurofibrillary tangles (NFTs) and neuropil threads (NTs) segmentation is a challenging task, due to lack of ground truth segmentation data of these abnormal tau pathology. This problem is most prominent in the case of segmenting NTs, where the small threadlike morphology makes pixel-wise labeling a laborious task and unrealistic for large-scale studies. Lack of ground truth data poses a significant limitation for many learning-based methods to generate accurate segmentations of NFTs and NTs. This work presents an automated pipeline for pixel level segmentation of NFTs and NTs that does not rely on ground truth segmentation data. The pipeline is composed of four main steps: (1) color deconvolution is used to separate histopathology images into staining channels (DAB, Hematoxylin, and Eosin), (2) Otsu's thresholding is used on the DAB stain channel to generate pixel level segmentation of abnormal tau proteins staining, (3) a weakly-supervised learning paradigm (WildCat), using only global descriptors of images, is used to generate density maps of potential regions of NFTs and NTs, and (4) density maps and segmentations are then integrated using connected component analysis to localize NFTs and NTs in the detected tau segmentations. Our results show high global classification accuracy for NFTs (Acc:0.96) and NTs (Acc:0.91), and statistically significant distinctions when evaluating the percent area occupied of the detected NTs relative to expert ratings of NTs severity. Qualitative assessment of the NFTs and NTs results showed accurate pixel-level segmentations of the NFTs, while modest performance for NTs.
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