Hadwiger, M. | bioRxiv Science

bioRxiv2024

niiv: Fast Self-supervised Neural ImplicitIsotropic Volume Reconstruction

Three-dimensional (3D) microscopy data often is anisotropic with significantly lower resolution (up to 8x) along the z axis than along the xy axes. Computationally generating plausible isotropic resolution from anisotropic imaging data would benefit the visual analysis of large-scale volumes. This paper proposes niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data that can quickly produce images at arbitrary output resolutions. The representation embeds a learned latent code within a neural field that describes the implicit higher-resolution isotropic image region. We use a novel attention-guided latent interpolation approach, which allows flexible information exchange over a local latent neighborhood. Under isotropic volume assumptions, we self-supervise this representation on low-/high-resolution lateral image pairs to reconstruct an isotropic volume from low-resolution axial images. We evaluate our method on simulated and real anisotropic electron (EM) and light microscopy (LM) data. Compared to a state-of-the- art diffusion-based method, niiv shows improved reconstruction quality (+1 dB PSNR) and is over three orders of magnitude faster (1,000x) to infer. Specifically, niiv reconstructs a 1283 voxel volume in 2/10th of a second, renderable at varying (continuous) high resolutions for display.

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bioRxiv2022

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

UO_SCPLOWLTRALISERC_SCPLOW is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. UO_SCPLOWLTRALISERC_SCPLOW implements a high performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of UO_SCPLOWLTRALISERC_SCPLOW is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. UO_SCPLOWLTRALISERC_SCPLOW is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SignificanceThere is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present UO_SCPLOWLTRALISERC_SCPLOW, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations. Key pointsO_LIUltraliser creates spatial models of neuro-glia-vascular (NGV) structures with realistic geometries. C_LIO_LIUltraliser creates high fidelity watertight manifolds and large scale volumes from centerline descriptions, non-watertight surfaces, and binary masks. C_LIO_LIResulting models enable scalable in silico experiments that can probe intricate structure-function relationships. C_LIO_LIThe framework is unrivalled both in ease-of-use and in the accuracy of resulting geometry representing a major leap forward in simulation-based neuroscience. C_LI

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