Installation Instructions | |
▼Getting Started | The following sections give a high-level overview of CaPTk, with the intention of familiarizing a new user with the platform functionalities and interface |
Keyboard Shortcuts | These are the keyboard shortcuts available on CaPTk: |
Components | CaPTk has been designed as a modular platform, currently incorporating components for:
- User interaction (e.g., coordinate definition, region annotation, and spherical approximation of abnormalities)
- Image pre-processing (e.g., smoothing, bias correction, co-registration, normalization)
- Image segmentation
- Feature extraction
- Specialized diagnostic analysis
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Supported Images | Currently, CaPTk supports visualization of MR, CT, PET, X-Ray and Full-field Digital Mammography (FFDM) images in both NIfTI (i.e., .nii, .nii.gz) and DICOM (i.e., .dcm) formats |
Image Loading | The File -> Load menu is used to load all image types |
Image Visualization | Sliders on each visualization panel (highlighted in green in the figure below) control the movement across respective axes |
Tab Docking (Windows-only) | Double clicking on the tab bar will dock/undock the entire section, resulting in larger visualization panels in your monitor |
Coordinate definition (Seed-point initialization) | The Seed Points tab includes two general types of initialization (i.e., tumor and tissue points), controlled by the respective radio buttons |
Label Annotation/Drawing Panel | This panel provides the ability to annotate Regions of Interest (ROIs) and save them as masks |
Pre-processing | Image pre-processing is essential to quantitative image analysis |
Utilities (Command-line only) | To make pipeline construction using CaPTk easier, a bunch of utilities have been provided |
Segmentation | Currently there are two ways to produce segmentation labels for various structures in the images that are loaded:
- using the Geodesic Distance Transform
- using ITK-SNAP
- Skull Stripping (Deep Learning based)
- Tumor Segmentation (Deep Learning based)
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Feature Extraction | The feature extraction tab in CaPTk enables clinicians and other researchers to easily extract feature measurements, commonly used in image analysis, and conduct large-scale analyses in a repeatable manner |
Specialized Applications | Various Specialized Applications are currently incorporated in CaPTk focusing on brain tumors, breast cancer, and lung nodules as shown in the figure below |
▼How To Guides | This section provides step-by-step guidance to apply the CaPTk functionalities: |
▼Pre-processing | Contains the following applications: |
DICOM to NIfTI conversion | This tool converts a DICOM series into the Neuroimaging Informatics Technology Initiative (NIfTI) file format |
Image Co-registration | This tool registers multiple moving images to a single target image using the Greedy Registration technique [1] |
Denoise-SUSAN (ITK filter) | This tool smooths an image already loaded in CaPTk, to remove any high frequency intensity variations (i.e., noise), using the SUSAN algorithm [1] |
N4 Bias Correction (ITK filter) | This tool corrects an image, already loaded in CaPTk, for magnetic field inhomogeneities using a non-parametric method [1] |
Histogram Matching | This tool normalizes the intensity profile of an input image based on the intensity profile of a reference image [1] |
Z-Scoring Normalization | This tool does a z-scoring based normalization on the loaded image |
Segmentation | |
Feature Extraction | REQUIREMENTS:
- An image or a set of co-registered images
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▼Specialized Applications (SAs) Usage | Contains the following applications: |
Brain Cancer: WhiteStripe Normalization | This algorithm normalizes conventional brain MRI scans [1] by detecting a latent subdistribution of normal tissue and linearly scaling the histogram of images |
Brain Cancer: Population Atlas | This application facilitates the users to generate population atlases for patients with different tumor subgroups [1] to emphasize their heterogeneity |
Brain Cancer: Confetti | This is a method for automated extraction of white matter tracts of interest in a consistent and comparable manner over a large group of subjects without drawing the inclusion and exclusion ROIs, facilitating an easy correspondence between different subjects, as well as providing a representation that is robust to edema, mass effect, and tract infiltration [1-3] |
Brain Cancer: Glioblastoma Infiltration Index (Recurrence) | This application provides a probability map of deeply infiltrating tumor in the peritumoral edema/invasion region that largely agrees with subsequent recurrence in de novo glioblastoma patients, via multi-parametric MRI analysis, as shown in [1-3] |
Brain Cancer: Pseudoprogression Infiltration Index | This application provides an estimate of the pseudo-progression after radiotherapy in glioblastoma patients, via multi-parametric MRI analysis, as shown in [1] |
Brain Cancer: Glioblastoma Survival Prediction Index | This application provides the survival prediction index (SPI) of de novo glioblastoma patients by using baseline pre-operative multi-parametric MRI analysis [1] |
Brain Cancer: Glioblastoma EGFRvIII Surrogate Index (PHI Estimator) | This application evaluates the Epidermal Growth Factor Receptor splice variant III (EGFRvIII) status in primary glioblastoma, by quantitative pattern analysis of the spatial heterogeneity of peritumoral perfusion dynamics from Dynamic Susceptibility Contrast (DSC) MRI scans, through the Peritumoral Heterogeneity Index (PHI / φ-index) [1-3] |
Brain Cancer: Glioblastoma EGFRvIII SVM Index | This application provides the detection of EGFRvIII mutation status of de novo glioblastoma patients by using baseline pre-operative multi-parametric MRI analysis [1] |
Brain Cancer: Glioblastoma Molecular Subtype Prediction | This application provides the prediction of the molecular subtype of de novo glioblastoma patients using baseline pre-operative multi-parametric MRI analysis [1] |
Breast Cancer: Breast Density Estimation (LIBRA) | The Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) is a software application for fully-automated breast density segmentation in full-field digital mammography (FFDM) [1] |
Breast Cancer: Texture Feature Extraction | This application extracts the Texture Features as described in the paper [1] |
Breast Cancer: Breast Segmentation | This application uses LIBRA [1] to extract the breast region in the loaded image |
Lung Cancer: Radiomics Analysis of Lung Cancer | This application provides a fully automatic segmentation of lung nodules and prediction of survival and nodal failure risks as a three step workflow[1] |
Brain Cancer: Directionality Estimator | This application estimates the volumetric changes and their directionality for a given ROI across two timepoints [1] and also returns the projections of the boundary point with the maximum distance from a seedpoint (with label TU) in each of the 3 2D visualized planes |
Miscellaneous: Perfusion Alignment | This application does perfusion alignment of the input Dynamic-Susceptibility Contrast-enhanced (DSC) MRI scan |
Miscellaneous: Perfusion Derivatives | This application extracts various measurements from a Dynamic-Susceptibility Contrast-enhanced (DSC) MRI scan |
Miscellaneous: Diffusion Derivatives | This application extracts various measurements from a Diffusion Weighted MRI scan |
Miscellaneous: Training Module | This applicatiparameterizeachine">Support Vector Machineparameterize |
Miscellaneous: PCA Volume Extraction | This application extracts the principal components from DSC-MRI scans as mentioned in [1] |
▼Scientific Findings using CaPTk | This section presents examples of applications using CaPTk: |
Non-invasive Imaging Biomarker of EGFRvIII in Glioblastoma Patients | |
Prediction of Overall Survival in Glioblastoma Patients | |
Probability Maps of Potential Recurrence of Glioblastoma Tumors | |
Imaging Biomarkers Related to Cancer Risk and Development of Breast Cancer | |
▼Technical Reference | This section gives further technical details for all previous documentation material |
Further Application Details and Assumptions | Contents |
Build CaPTk from Source | Source code for the CaPTk graphical interface and applications is distributed for sites that wish to examine the code, collaborate with CBICA in future development, and for compatibility |
For Developers | Contents |
Download Instructions | Visit our Download Page hosted in NIH-supported NITRC (https://www.nitrc.org/frs/?group_id=1059), to download the CaPTk source code and binaries |
Changelog: Release Notes | |
People (Credits) |
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