Rootlets-based registration to the PAM50 spinal cord template. [PDF]
Bédard S +4 more
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Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study. [PDF]
Di Noto T +11 more
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MorphoNet 2.0: An innovative approach for qualitative assessment and segmentation curation of large-scale 3D time-lapse imaging datasets. [PDF]
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Exploring fern pathosystems and immune receptors to bridge gaps in plant immunity. [PDF]
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Bacterial chromatin remodeling associated with transcription-induced domains at pathogenicity Islands. [PDF]
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Extracellular vesicles from glycolytic mesenchymal stromal cells restrain arthritis progression via IL-10-Producing T and B cells. [PDF]
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Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT. [PDF]
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3D manipulation of motion imagery
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002We present a set of automatic methods for the recovery and refinement of 3D scene geometry and camera poses from motion imagery. First, we present a two-frame "direct" method, which simultaneously estimates both relative pose between the cameras and 3D scene geometry using information from the images alone.
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3D-segmentation and display of tomographic imagery
[1988 Proceedings] 9th International Conference on Pattern Recognition, 2003Using the Marr-Hildreth operator with a subsequent closing algorithm, it is possible to determine the main constituents of the human head from magnetic resonance image data. With use of the segmentation results in a generalized voxel model one can generate images that are very similar to what is known from real anatomy.
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Knowledge-Guided Segmentation of 3D Imagery
Graphical Models and Image Processing, 1996This paper presents a computationally efficient and robust approach to locate, label, and isolate three-dimensional (3D) structures from discrete 3D imagery. The emphasis is placed on extracting a convex, singly connected 3D structure of interest imbedded in discrete, volumetric data sets that are sparse, noisy, and possibly misleading.
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