Publications by authors named "Norman Rzepka"

Article Synopsis
  • Magnetic resonance imaging (MRI) is essential for detecting ischemic stroke lesions in mice, but manual segmentation is difficult and inconsistent.
  • A new automated segmentation method using deep learning simplifies this process by working directly with raw MRI scans, minimizing preprocessing steps.
  • Results show that the automated method achieves higher accuracy in lesion segmentation compared to manual methods, reducing individual bias and standardizing results across research studies.
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The growing size of EM volumes is a significant barrier to findable, accessible, interoperable, and reusable (FAIR) sharing. Storage, sharing, visualization and processing are challenging for large datasets. Here we discuss a recent development toward the standardized storage of volume electron microscopy (vEM) data which addresses many of the issues that researchers face.

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A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process.

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A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process.

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Understanding the function of biological tissues requires a coordinated study of physiology and structure, exploring volumes that contain complete functional units at a detail that resolves the relevant features. Here, we introduce an approach to address this challenge: Mouse brain tissue sections containing a region where function was recorded using in vivo 2-photon calcium imaging were stained, dehydrated, resin-embedded and imaged with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT). SXRT provided context at subcellular detail, and could be followed by targeted acquisition of multiple volumes using serial block-face electron microscopy (SBEM).

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We report webKnossos, an in-browser annotation tool for 3D electron microscopic data. webKnossos provides flight mode, a single-view egocentric reconstruction method enabling trained annotator crowds to reconstruct at a speed of 1.5 ± 0.

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