Skip to main content
100%

Gist 5dd4cc899220d90a2606a1f3376b5f6f

✓ Published0🌍 Public
Aakeshavan
Last edited May 2, 2016
Created on May 2, 2016

This visualization demonstrates signal-to-noise ratio (SNR) calculation from ADNI phantom MRI data using a Jupyter notebook. It loads NIfTI images with nibabel, applies Otsu thresholding and histogram-based binning to segment structures, then labels connected components with scipy.ndimage. The workflow includes FOV validation, quaternion-based RAS coordinate transformation, and voxel volume checks using custom functions like `bin_data` and `clean_labels`, with matplotlib and prettyplotlib used for plotting. The notebook processes phantom scans to identify regions meeting required volume thresholds.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 237fa77ce0984eaedbfd

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.

Aakeshavan
77% match
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
75% match
Loading thumbnail…

Gist dd6562218ed80aaca786

This example demonstrates a spectral clustering workflow for neuroimaging data, using scikit-learn’s `SpectralClustering` to segment thalamus voxels into distinct clusters based on correlation matrices. It processes fMRI time-series data via Nipype pipelines, nibabel for image I/O, and matplotlib with `%pylab inline` for visualization. The notebook shows how clustering results are saved as NIfTI files, with parameters like subject ID, region of interest, and cluster count varied across runs.

Aakeshavan
74% 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
73% 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
73% match
Loading thumbnail…

Gist d8aa62e81e12e25d45a3

This visualization presents statistical calculations for brain imaging data, comparing reliability metrics across different analysis methods. The notebook uses Python with rpy2 to bridge R and Python, calling the `psych` package for intraclass correlation coefficients and `statsmodels` for additional statistical modeling. It generates plots with matplotlib and seaborn, applying custom chart-cleaning functions to remove unnecessary axes and gridlines. The code also leverages pandas for data manipulation and NumPy for z-score normalization, with output rendered inline through IPython’s `%pylab` magic.

Aakeshavan
73% match
Loading thumbnail…

Gist 0efb8d4b7f4538ca9efc78832536d0c3

This visualization compares MRIQC automated quality metrics with manual voting results for T1-weighted brain scans. It processes JSON vote data from multiple raters, consolidating their quality assessments into a pandas DataFrame. The example demonstrates how to aggregate and inspect manual quality-control annotations alongside automated MRIQC scores, using pandas for data manipulation and matplotlib for inline plotting in a Jupyter notebook.

Aakeshavan
72% match
Loading thumbnail…

Gist 9678802

This interactive visualization simulates MRI brain volume data from two scanner sites, comparing healthy controls and multiple sclerosis patients across age and disease severity. It applies linear calibration transformations between sites and tests statistical models, including OLS for controls and robust linear regression for patients. Built with Vue and WebGL, the notebook uses Python with matplotlib, seaborn, and pandas to generate and analyze the synthetic datasets, displaying distributions and regression fits interactively.

Aakeshavan
72% match