Skip to main content
100%

Gist 237fa77ce0984eaedbfd

✓ Published0🌍 Public
Aakeshavan
Last edited Oct 11, 2014
Created on Oct 11, 2014

A Vue-based visualization of brain MRI segmentation data, this example loads anatomical and labeled brain volumes using nibabel and renders them with nipy's OrthoSlicer. The notebook applies a threshold to isolate specific cortical regions, remaps label values, and displays the result as a colored overlay on the anatomical scan using an HSV colormap, producing a single axial slice visualization of segmented brain structures.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 5c36e7318dd5c85aa743

This visualization displays a 16-slice mosaic of brain MRI scans across three orthogonal planes (x, y, z), with yellow contour lines outlining anatomical labels from a segmentation. The code loads NIfTI brain images and masks via nibabel, masks background data with NumPy masked arrays, and uses nipy’s `plot_anat` and `contour_map` to render slices with matplotlib subplots. It computes bounding boxes from the affine transformation to select meaningful cut coordinates, then arranges the slices in 4×4 grids per plane. The function supports optional contouring and saving each mosaic as a PNG image.

Aakeshavan
85% match
Loading thumbnail…

Gist b67c03f2dc7fc6d38bf4b732214d6589

An interactive 3D volume rendering of structural MRI brain data, created with IPyvolume and its pylab interface. The notebook loads NIfTI images via nibabel, first displaying a defaced scan with translucent opacity levels, then a full T1-weighted image from OSF. The visualization uses `p3.figure` to configure camera properties, hide axes and box, and set coordinate limits, with `p3.volshow` rendering the volume. Users can rotate and inspect the brain in three dimensions through the Jupyter widget.

Aakeshavan
82% match
Loading thumbnail…

Gist 1ac505c846b09faa7bc4

This visualization demonstrates the variability of FreeSurfer-derived brain structure measurements across different MRI scanner models and sites. It plots the volume values for anatomical regions like the lateral ventricle and cerebellum, revealing distribution patterns and potential batch effects. Built with a Vue framework, the notebook uses pandas for data manipulation, matplotlib and seaborn for statistical plotting, and processes a CSV file containing subject-level neuroimaging metrics. The display emphasizes comparative analysis of brain region volumes across scanning conditions.

Aakeshavan
79% match
Loading thumbnail…

Gist 42b954f71c1668a26572

This example visualizes longitudinal Freesurfer brain imaging data, showing how volumes of cerebrospinal fluid, white matter, gray matter, and whole brain, along with mean cortical thickness, change across time points and scanner types. The code loads CSV tables into pandas DataFrames, aggregates specific anatomical regions into tissue categories, and merges hemisphere thickness values. It uses matplotlib, seaborn, and statsmodels for plotting and statistical modeling, with the data prepared through pandas operations before visualization.

Aakeshavan
79% match
Loading thumbnail…

Gist 8e62843a6ffa1dfd7293

This visualization shows how brain tissue volumes (CSF, WM, GM, and total brain) change across multiple time points and scanner types, with each subject’s measurements pivoted into a comparative table. The code uses pandas for data wrangling, including `pivot_table` and column aggregation, and relies on `seaborn` and matplotlib’s `%pylab inline` for plotting. Data comes from SIENAX report files parsed into a DataFrame, then normalized to cubic centimeters. The final table displays time point and scanner metadata alongside the computed tissue volumes.

Aakeshavan
79% match
Loading thumbnail…

Gist 51ee3c0f179008ff4a15

This visualization demonstrates white-stripe normalization of a T1-weighted brain MRI, overlaying a colored white-stripe mask on the normalized anatomical image. The notebook imports nipy’s `plot_map` for rendering, and uses scikit-image’s `threshold_otsu` to mask background voxels. It processes a control subject’s N4-corrected, rigidly aligned image via an R script (WhiteStripe package) called through rpy2, then visualizes the mask at the stripe’s centroid coordinates derived from the affine transform. The rendering uses a HSV colormap for the stripe overlay.

Aakeshavan
78% match
Loading thumbnail…

Gist 5f27eb42f27c58d522f1210fd3659f06

This visualization displays brain imaging results for a single subject, comparing fractional anisotropy (APM), mean diffusivity (MD), and B0 maps before and after a procedure. It automatically locates the world coordinates of the maximum APM difference and generates six static PNG plots, each showing a statistical map overlaid on a pre-APM background. The code uses Nilearn’s `plot_stat_map` and `plot_anat` functions with a jet colormap and black background, alongside NumPy for coordinate transforms and Matplotlib for figure styling.

Aakeshavan
77% match
Loading thumbnail…

Gist 61f729d5fdcda29e57ebc8ec0b06652f

This example generates image tiles from 3D neuroimaging data, converting MRI volumes into 2D slice images along a specified axis. It loads NIfTI files with nibabel, reorients the data to IPL coordinates, and renders each slice as a grayscale PNG using matplotlib. The code also creates JSON manifests that record slice indices, mask information, and file paths, enabling tile-based visualization of brain scans.

Aakeshavan
77% match