3D Slicer as an Image Computing Platform for the Quantitative Imaging Network methods
Aim. Evidence-backed execution summary for 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network methods from 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network.
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On this page
This experiment, in seven questions
Jump straight to the part of the recipe you need. Data and provenance labels stay close to the action they support.
Shopping and prep list
What do I need before I start?
human
Subject model for the experiment.
- Use
- confirm full cohort details in the source paper
Overview of 3D Slicer
reagent used in the protocol.
- Use
- Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distributed as part of the NA-MIC Kit, a collection of the software tools supported in part by the National Alliance for Medical Image Computing (NA-...
Overview of 3D Slicer
Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve custom image processing software. The role of software evolves over the different stages of the imaging biomarker lifecycle. In the inception s...
- Use
- Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve custom image processing software. The role of software evolves over the different stages of the imaging biomarker lifecycle. In the inception s...
Overview of 3D Slicer
The architecture of 3D Slicer follows a modular and layered approach, as shown in. At the lower level of the architecture are the fundamental libraries provided by the operating system that are not packaged with Slicer, such as OpenGL and hardware drivers that allow efficient usage of the windowing and graphics res...
- Use
- The architecture of 3D Slicer follows a modular and layered approach, as shown in. At the lower level of the architecture are the fundamental libraries provided by the operating system that are not packaged with Slicer, such as OpenGL and hardware drivers that allow efficient usage of the windowing and graphics res...
Overview of 3D Slicer
Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distributed as part of the NA-MIC Kit, a collection of the software tools supported in part by the National Alliance for Medical Image Computing (NA-...
- Use
- Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distributed as part of the NA-MIC Kit, a collection of the software tools supported in part by the National Alliance for Medical Image Computing (NA-...
Overview of 3D Slicer
3D Slicer itself consists of the lean application core, Slicer Modules, and Slicer Extensions. The core implements the Slicer user interface, provides support for data input/output (IO), visualization and developer interfaces that support extension of the application with new plugins. Internally, Slicer uses a scene...
- Use
- 3D Slicer itself consists of the lean application core, Slicer Modules, and Slicer Extensions. The core implements the Slicer user interface, provides support for data input/output (IO), visualization and developer interfaces that support extension of the application with new plugins. Internally, Slicer uses a scene...
Overview of 3D Slicer
Since its inception in late 1990s, 3D Slicer has been evolving with major architectural, functional and GUI redesigns occurring every 4-5 years. The current (fourth) generation of Slicer was released in November 2011. The most notable improvements of the software as compared to the previous (third) version are...
- Use
- Since its inception in late 1990s, 3D Slicer has been evolving with major architectural, functional and GUI redesigns occurring every 4-5 years. The current (fourth) generation of Slicer was released in November 2011. The most notable improvements of the software as compared to the previous (third) version are...
Overview of 3D Slicer
Within each generation, new and updated releases of Slicer are prepared every 2 to 6 months. These releases include performance improvements, bug fixes and new functionality, but no major changes to the base architecture or GUI. A release includes a tagged version of the source code in the source code repository as...
- Use
- Within each generation, new and updated releases of Slicer are prepared every 2 to 6 months. These releases include performance improvements, bug fixes and new functionality, but no major changes to the base architecture or GUI. A release includes a tagged version of the source code in the source code repository as...
Clinical Research Platform
3D Slicer is available in the form of binary packages that have platform-specific installers for all major operating systems. These packages are self-contained, which means they include all the dependency libraries and toolkits needed to use Slicer on a given platform. This also makes it possible to run two or more...
- Use
- 3D Slicer is available in the form of binary packages that have platform-specific installers for all major operating systems. These packages are self-contained, which means they include all the dependency libraries and toolkits needed to use Slicer on a given platform. This also makes it possible to run two or more...
Clinical Research Platform
3D Slicer visualization capabilities support various imaging modalities (e.g., CT, PET, MRI and Ultrasound) and can be used for visualization of 2-, 3- and 4-dimensional datasets. Support of 3-dimensional datasets (MRI, CT, PET) has enjoyed most attention based on the substantial number of use cases and resulting in...
- Use
- 3D Slicer visualization capabilities support various imaging modalities (e.g., CT, PET, MRI and Ultrasound) and can be used for visualization of 2-, 3- and 4-dimensional datasets. Support of 3-dimensional datasets (MRI, CT, PET) has enjoyed most attention based on the substantial number of use cases and resulting in...
Modules
Software used for acquisition, scoring, statistics, or reporting.
- Use
- The SEM XML compliant communication interface is the only requirement imposed on the SEM modules by the Slicer application. SEM modules can be implemented as independent executable files, shared libraries or scripts (such as Python or MATLAB scripts) that via SEM can leverage Slicer visualization and DICOM capabilit...
Before you run
What should be confirmed before execution?
First confirmation
Equipment is listed but no product mappings are linked.
Confirm before execution
This page is backed by a publishable Replication Data Ledger package with zero critical source-verification issues.
Confirm before execution
Open the source paper before finalizing run-specific details.
Procurement checkpoint
Use source-stated vendors where present. Treat mapped products as sourcing options unless the page marks an exact source match.
Open quote workflowStep-by-step procedure
What do I do, in order?
Modules
Image Guided Therapy (IGT): modules that support applications in image-guided therapy. The key functionality provided by the tools in this category is the OpenIGTLink interface that enables exchange of data between Slicer and external systems, such as robotic devices, MR scanners, and commercial image-guided surgery platforms.
Data Formats and Organization
Support of the widely accepted data exchange standards and interfaces are critical for any image computing tool to be useful in a clinical research environment. 3D Slicer supports import, query, retrieve, and storage of clinical images using DICOM protocols and data structures. These features allow data exchange with clinical systems such as scanners, workstations, and PACS servers. Example use cases for DICOM networking include: (1) setting up a scanner to route newly acquired images directly to a DICOM listener in Slicer to support image guided therapy; (2) using Slicer to query and retrieve DICOM studies in a departmental PACS to perform retrospective image analysis; and (3) sending derived (post-processed) images to a PACS system to become part of the patient record. In all cases, coordination with responsible clinical support staff is required to ensure that research use of Slice...
Data Formats and Organization
As images are imported into Slicer, they are added to the Slicer scene that is used to organize the individual data elements. While the user performs operations on the data or reconfigures the visualization elements of the interface, the scene is used to keep track of the application configuration. Scene views cache the complete state of the Slicer application, including the configuration and content of the viewers, together with a screen capture of the visualization elements and a textual description. Slicer scenes can contain multiple scene views that emphasize or communicate different aspects of data or different stages of its processing. Scenes can be stored on disk using the MRML format for sharing of the analysis results and to facilitate reproducibility of the observation. As an example, a scene view can correspond to a visualization that was used to prepare a certain figure in...
Modules
Compared to SEM plugins, Slicer loadable modules have complete access to the Slicer core logic, GUI and MRML elements. Loadable modules are typically developed for interactive tools, or for those applications that require new MRML data types, event handling or customization of the main Slicer GUI. Loadable modules follow the same MVC design pattern as the application core by introducing module-specific Logic, as well as GUI and MRML classes. Logic classes are typically the most important component for the developers of the new image analysis tools, as they include the core computation and the implementation of the analysis algorithms. Such an implementation would usually (although this is not a requirement) rely on the Insight Toolkit and VTK for constructing pipelines and implementing lower level processing steps. The View elements allow the module to interact with the operator to in...
Iowa
Response assessment to cancer therapy based on molecular imaging with PET/CT lacks an accepted robust and accurate quantitative metric. The literature is rife with new and different ways to analyze PET/CT image data, including variants of standardized uptake values (SUV), various lesion-to-background approaches, volume measurement, and metabolic tumor volume, but it is still unclear which of these approaches is best suited for response assessment.
MGH
3D Slicer provides a number of state-of-the-art algorithms for rigid, affine and deformable registration (in addition to manual registration). These algorithms can be selected and optimized depending on the anatomical site (e.g., brain vs prostate), purpose (multimodal vs longitudinal vs registering to an atlas), performance (speed vs accuracy vs robustness), and level of interaction (e.g., use of fiducials or markers). The registration tools within Slicer are utilized in a number of ways. Regions of interest delineated on T1w, T2w or FLAIR MRI are registered to the other imaging modalities and associated parametric maps (e.g., K trans maps from DCE, rCBV maps from DSC and ADC maps from DTI) as seen in. This capability helps better understand the distribution of these parameters in the different regions of the tumor and in different patients. These tools can also be used to register...
Measurement outputs
What raw and processed outputs should exist?
Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve cus...
- Raw artifact
- Field or section images captured from matched samples
- Processed artifact
- Selected representative panels with quantified intensity, counts, or area measurements
- Reported as
- Per-group imaging summaries with representative figures and quantified endpoints
One of the goals of 3D Slicer is to provide a common set of base functionality to facilitate development and validation of the medical image computing methods for the "can...
- Raw artifact
- Field or section images captured from matched samples
- Processed artifact
- Selected representative panels with quantified intensity, counts, or area measurements
- Reported as
- Per-group imaging summaries with representative figures and quantified endpoints
The use of an image analysis tool in a clinical research environment introduces new requirements. Support of the DICOM standard for communicating image data is commonly required...
- Raw artifact
- Field or section images captured from matched samples
- Processed artifact
- Selected representative panels with quantified intensity, counts, or area measurements
- Reported as
- Per-group imaging summaries with representative figures and quantified endpoints
Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distribut...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
Analysis plan
How should the outputs become interpretable results?
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
inferred from protocolPreprocessing / cleaning
Segmentation: tools that separate individual sub-regions in the dataset based on certain features.
from paperScoring or quantification
Quantify the primary readouts for this experiment: Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve cus...; One of the goals of 3D Slicer is to provide a common set of base functionality to facilitate development and validation of the medical image computing methods for the "can...; The use of an image analysis tool in a clinical research environment introduces new requirements. Support of the DICOM standard for communicating image data is commonly required...; Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distribut....
from paperStatistical comparison
Segmentation: tools that separate individual sub-regions in the dataset based on certain features. Most of the tools in this category operate on single- or multi-channel images...; The event-driven architecture of the Slicer core is complex in part due to the fact that the application incorporates several libraries (Qt, VTK and MRML) that have their distin...; The Slicer Execution Model (SEM) implements the simplest approach, as it does not require any knowledge of the 3D Slicer architecture, and does not have dependencies on the Slic...; The SEM XML compliant communication interface is the only requirement imposed on the SEM modules by the Slicer application. SEM modules can be implemented as independent executa...
from paperReporting output
Report representative outputs alongside summary comparisons for Computerized image analysis plays an increasing role in supporting clinical and research needs. Promising methodologies that may lead to new imaging biomarkers often involve cus..., One of the goals of 3D Slicer is to provide a common set of base functionality to facilitate development and validation of the medical image computing methods for the "can..., The use of an image analysis tool in a clinical research environment introduces new requirements. Support of the DICOM standard for communicating image data is commonly required..., Some of the libraries contributing to the foundation of 3D Slicer are designed in close collaboration and often share the same developer community. These libraries are distribut....
inferred from protocolStructured statistical methods
Segmentation: tools that separate individual sub-regions in the dataset based on certain features. Most of the tools in this category operate on single- or multi-channel images...; The event-driven architecture of the Slicer core is complex in part due to the fact that the application incorporates several libraries (Qt, VTK and MRML) that have their distin...; The Slicer Execution Model (SEM) implements the simplest approach, as it does not require any knowledge of the 3D Slicer architecture, and does not have dependencies on the Slic...; The SEM XML compliant communication interface is the only requirement imposed on the SEM modules by the Slicer application. SEM modules can be implemented as independent executa...
source structuredSource and audit
What supports the facts on this page?
Evidence quotes (6)
Image Guided Therapy (IGT): modules that support applications in image-guided therapy. The key functionality provided by the tools in this category is the OpenIGTLink interface that enables exchange of data between Slicer and external systems, such as robotic devices, MR scanners, and commercial image-guided surgery platforms.
Support of the widely accepted data exchange standards and interfaces are critical for any image computing tool to be useful in a clinical research environment. 3D Slicer supports import, query, retrieve, and storage of clinical images using DICOM protocols and data structures. These features allow data exchange with clinical systems such as scanners, workstations, and PACS servers. Example use cases for DICOM networking include: (1) setting up a scanner to route newly acquired images directly to a DICOM listener in Slicer to support image guided therapy; (2) using Slicer to query and retrieve DICOM studies in a departmental PACS to perform retrospective image analysis; and (3) sending derived (post-processed) images to a PACS system to become part of the patient record. In all cases, coordination with responsible clinical support staff is required to ensure that research use of Slicer is authorized and has been tested to confirm that it will not interfere with ongoing clinical care. Implementation of the import and export of DICOM RT structures user in radiotherapy is currently under way.
As images are imported into Slicer, they are added to the Slicer scene that is used to organize the individual data elements. While the user performs operations on the data or reconfigures the visualization elements of the interface, the scene is used to keep track of the application configuration. Scene views cache the complete state of the Slicer application, including the configuration and content of the viewers, together with a screen capture of the visualization elements and a textual description. Slicer scenes can contain multiple scene views that emphasize or communicate different aspects of data or different stages of its processing. Scenes can be stored on disk using the MRML format for sharing of the analysis results and to facilitate reproducibility of the observation. As an example, a scene view can correspond to a visualization that was used to prepare a certain figure in a report or manuscript. Accompanied by the Slicer scene, version of the software used and input dataset used to prepare it, such an article would provide a detailed provenance record, together with the methods, parameters and visualization context.
Compared to SEM plugins, Slicer loadable modules have complete access to the Slicer core logic, GUI and MRML elements. Loadable modules are typically developed for interactive tools, or for those applications that require new MRML data types, event handling or customization of the main Slicer GUI. Loadable modules follow the same MVC design pattern as the application core by introducing module-specific Logic, as well as GUI and MRML classes. Logic classes are typically the most important component for the developers of the new image analysis tools, as they include the core computation and the implementation of the analysis algorithms. Such an implementation would usually (although this is not a requirement) rely on the Insight Toolkit and VTK for constructing pipelines and implementing lower level processing steps. The View elements allow the module to interact with the operator to initialize the inputs and processing parameters, and any interactive initialization of the algorithm (e.g., collect the initialization seed points placed in the 2D slice viewer for a lesion segmentation tool). Examples of the interactive loadable modules available in 3D Slicer are "EMSegmenter&#...
Response assessment to cancer therapy based on molecular imaging with PET/CT lacks an accepted robust and accurate quantitative metric. The literature is rife with new and different ways to analyze PET/CT image data, including variants of standardized uptake values (SUV), various lesion-to-background approaches, volume measurement, and metabolic tumor volume, but it is still unclear which of these approaches is best suited for response assessment.
3D Slicer provides a number of state-of-the-art algorithms for rigid, affine and deformable registration (in addition to manual registration). These algorithms can be selected and optimized depending on the anatomical site (e.g., brain vs prostate), purpose (multimodal vs longitudinal vs registering to an atlas), performance (speed vs accuracy vs robustness), and level of interaction (e.g., use of fiducials or markers). The registration tools within Slicer are utilized in a number of ways. Regions of interest delineated on T1w, T2w or FLAIR MRI are registered to the other imaging modalities and associated parametric maps (e.g., K trans maps from DCE, rCBV maps from DSC and ADC maps from DTI) as seen in. This capability helps better understand the distribution of these parameters in the different regions of the tumor and in different patients. These tools can also be used to register the radiation therapy plans (CT-based) to the MR functional imaging, allowing exploration of the site of recurrence with respect to dose field and study the phenomenon of radiation necrosis. Registration algorithms and the change detection tools can be used to identify regions of change in the tumor...
Machine-readable layer
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