Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: Microvascular plasticity in mouse stroke model recovery: Anatomy statistics, dynamics measured by longitudinal in vivo two-photon angiography, network vectorization

doi: 10.1177/0271678X241270465

Figure Lengend Snippet: Experimental Methods: (a) Mouse cortex is imaged in vivo through a cranial window using a tiling protocol to cover as large of a volume as possible in a single imaging session. The stitched image is vectorized using the SLAVV software for reconstruction, visualization, and statistical analysis. The vessel directions (as well as the borders of the 2PM images and the ROI borders) are color-coded with respect to their alignment with the imaging coordinate system: ( xyz <->CMY). (b) LSCI is used to orient the 2PM imaging session to reproducibly image the same (∼1 mm 3 ) volume longitudinally over several imaging sessions at two-week intervals and at a depth greater than 600 micrometers. Displayed is an orthographic projection in the (optical) z- axis of a tiled image volume of a healthy control subject. Lateral orthographic projections show the longitudinal imaging reproducibility and (c) the longitudinal experiment is repeated around a photothrombotic injury. The infarct appears to be contained to a (yellow) circle with a radius of approximately 1 mm in the LSCI post-stroke image. The x-projected 2PM image reveals that a (transparent cyan) spherical ROI beneath the surface approximates the shape of the infarct. Scale bars are 200 μm.

Article Snippet: 2PM volumetric angiographs were vectorized using the Segmentation-less Automated Vascular Vectorization (SLAVV) software in MATLAB with two notable additions to improve throughput: (1) Machine learning assisted the manual curation task (2) A maximum resolution constraint was imposed to improve the computational efficiency for the larger vessels in the image.

Techniques: In Vivo, Imaging, Software, Control

Journal: PLoS Computational Biology

Article Title: Segmentation-Less, Automated, Vascular Vectorization

doi: 10.1371/journal.pcbi.1009451

Figure Lengend Snippet: The purpose of SLAVV is to vectorize vascular objects from raw three dimensional images. The first step of the method is to linearly filter the input image to form “energy” and “size” images, which enhance vessel centerlines and estimate vessel sizes, respectively. Next, vertices along the blood vessels are extracted as local minima of the three-dimensional energy image. Vertices are then connected by edges, which follow minimal energy trajectories. Finally, a graph theoretic representation of the vascular network is generated from the vertices and edges.

Article Snippet: The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub.

Techniques: Generated

Journal: PLoS Computational Biology

Article Title: Segmentation-Less, Automated, Vascular Vectorization

doi: 10.1371/journal.pcbi.1009451

Figure Lengend Snippet: A. Simulated images of varying quality are generated from the vector set from Image 3 shown in . Image quality is swept along contrast and noise axes, independently. Example maximum intensity projections are shown for three extremes of image quality (triangle: best quality, 4-point star: high noise, 5-point star: lowest contrast). The legend shows the labels for the four segmentation methods used in B-D. Images are vectorized using SLAVV with different amounts of Gaussian filter, f G (60, 80, or 100% of matched filter length). B. Vasculature is segmented from three simulated images using four automated approaches: thresholding either voxel intensity or maximum energy feature on edge objects produced by three automated vectorizations. Voxel-by-voxel classification strengths of thresholded vectorized objects or voxel intensities are shown as ROC curves for three of the seven input images. Note that the ROC curves for the energy feature of vectors do not have support for every voxel, because not every voxel is contained in an extracted vector volume. Operating points with maximal classification accuracy are indicated by circles in the bottom row of B and plotted in the top row of C&D across all input images. C&D. Bulk network statistics (length, area, volume, and number of bifurcations) were extracted from vectors or binary images resulting from maximal accuracy operating points. Performance metrics were plotted against CNR (image quality) for a (C) contrast or (D) noise sweep. Thresholding vectorized objects to segment vasculature demonstrated a greater robustness to image quality than thresholding voxel intensities. Surface area, length, and number of bifurcations were not extracted from binary images, because these images were topologically very inaccurate.

Article Snippet: The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub.

Techniques: Generated, Plasmid Preparation, Produced