It is the product of a decade-long collaboration between Paul Yushkevich, Ph. ITK-SNAP provides semi-automatic segmentation using active contour methods, as well as manual delineation and image navigation.
Compared to other, larger open-source image analysis tools, ITK-SNAP design focuses specifically on the problem of image segmentation, and extraneous or unrelated features are kept to a minimum. The design also emphasizes interaction and ease of use, with the bulk of the development effort dedicated to the user interface.
Version 3. Some of the core advantages of ITK-SNAP include: Linked cursor for seamless 3D navigation Manual segmentation in three orthogonal planes at once A modern graphical user interface based on Qt Support for many different 3D image formats, including NIfTI and DICOM Support for concurrent, linked viewing, and segmentation of multiple images Support for color, multi-channel, and time-variant images 3D cut-plane tool for fast post-processing of segmentation results Extensive tutorial and video documentation Compared to other, larger open-source image analysis tools, ITK-SNAP design focuses specifically on the problem of image segmentation, and extraneous or unrelated features are kept to a minimum.A chronic stroke-related lesion is a heterogeneous CSF-filled cavity surrounded by a rim of damaged tissue.
Lesions vary in shape and size. It is often challenging to work with lesion data because other brain structures, especially ventricles, can change size and shape after the brain injury. On a T1-weighted image, CSF is very dark and the associated area of neuronal damage is darker than surrounding healthy tissue. However, the damaged tissue may be very similar to grey matter, making identification of the lesion even more difficult.
Both the CSF-filled cavity and the associated damaged tissue should be considered lesion. In addition, we include ventricular enlargement as part of the lesioned tissue if the lesion and ventricle are contiguous as in the tutorial data. It can be valuable to mask lesions. Lesion masking involves creating a layer that covers the lesion and only the lesion.
In the picture below, you see an axial slice through a brain with a large lesion. On the right, we have covered the lesion with a red mask translucent so you can see what is underneath. Most often we mask lesions manually. Lesion masks are sometimes referred to as lesion maps or as segmentations. Manual masking of lesions can take hours or days because we need to outline the lesion carefully on each slice of the image.
To determine whether an area should be included in the lesion mask, compare the tissue on the damaged side to the normal tissue in the other hemisphere. Here we review a semi-automated approach to segmentation of stroke-related lesions. For more tips on working with large lesions, see the Stroke Lesions page.
In this pipeline we fill the lesion in the left hemisphere without worrying about leaking into ventricles or outside the brain, because we will fix those problems later using FSL. It consists of one native space defaced T1 weighted anatomical image of a patient with a large stroke-related lesion. Anatomical MRI data like this must have the face removed in order to deidentify it before posting it publicly.
More information about how the data were collected is available here:. Kielar, A. Functional reorganization of language networks for semantics and syntax in chronic stroke: Evidence from MEG. Human Brain Mapping, 37 8 See Load and View Images. The Threshold mode we use is the first of the 3 icons yellow box, figure above : the complete waveform represents having both an upper and lower threshold. Choose each of the other icons to understand how they control thresholding. Thresholding works well when you have a single high quality image, as provided for this tutorial.
However, if you have multimodal data e.You seem to have CSS turned off. Please don't fill out this field. Do you have a GitHub project? Now you can sync your releases automatically with SourceForge and take advantage of both platforms.
Please provide the ad click URL, if possible:. Oh no! Some styles failed to load. Help Create Join Login. Operations Management. IT Management. Project Management. Resources Blog Articles Deals.
ITK-SNAP Release Notes - Version 3.6.0
Menu Help Create Join Login. Add a Review. Get project updates, sponsored content from our select partners, and more. Full Name. Phone Number. Job Title. Company Size Company Size: 1 - 25 26 - 99 - - 1, - 4, 5, - 9, 10, - 19, 20, or More.
Important new features include the ability to load multiple images of different dimensions, voxel size, and orientation into a single ITK-SNAP window; automatic and manual registration ; and enhanced support for DICOM format images. Another important new feature in this release is the ability to interpolate segmentation between slices.
This makes it possible to create manual segmentations much more quickly than before. When additional images are loaded, they are represented in memory in their native resolution, and resampled on the fly to match the screen resolution. This means that you can use information from two MRI modalities to guide manual segmentation. You can load a T1-weighted image with 1.
The tool provides both interactive manual registration and automatic affine and rigid registration. Rotation is performed by turning a 'wheel' widget, and very small rotations are possible. The center of rotation can be set by moving the cursor. Automatic registration is quite fast.
It allows rigid and affine registration. It supports mutual information inter-modality and patch cross-correlation intra-modality metrics. Optionally, a mask can be provided, over which the metric is computed.
It is easy to generate masks using the segmentation interpolation tool see below. Masks are useful when the extent of the images is different, e.This section gives step by step instructions on segmenting an image using the region competition snake in last section's terminology, snake evolution that uses the region feature image.
This section assumes that you are working with the image MRIcrop -orig. We will segment the caudate nucleus and the ventricles in this image.
This section also assumes that you are using the label file MRIcrop -seg. You can, however, follow the general directions of this section using a different image, but you will have to use your own judgement in selecting various parameters. In Section 4we have created a manual segmentation.
In order to perform the segmentation automatically, we will discard the manual segmetnation. This involves reloading the greyscale image. We will be segmenting the caudate nucleus. We have to make sure that the appropriate combination of the current drawing label and background draw over label is selected. The automatic segmentation component of SNAP requires a lot of computer resources. Both the amount of memory and the time required to compete a segmentation can be reduces by selecting a sub-region of the image on which to perform segmentation.
In this step, we will select a subregion of the image that contains the caudate nucleus. As you select the snake tool, a pink-colored dashed selection box will appear at the border of the each slice in the slice windows, as shown below:.
The selection box displays the region of interest that will be used in automatic segmentation. The use of word region here should not be confused with region competition.
The region of interest is a rectalinear box, while the regions in region competition are of arbitrary shape and are defined by uniform intensity. We will now adjust the region of interest by dragging the sides of the selection box. Notice that the tool options control subpanel contains two buttons: Reset Region and Segment 3D.ITK-SNAP Brain Tumor Segmentation Protocol
The former is used to reset the region of interest to the entire image. The second is used to enter the automatic segmentaiton mode of SNAP.
Recall the concept of edge and region competition feature images from the last section. In this step we will construct a region competition feature image appropriate for segmenting the caudate nuclei.
First let's tell SnAP which type of the feature image we will be using:. Now, let's estimate the range of intensities to which the voxels in the caudate nucleus belong.
You will find that the intensities in the caudate range between the high 40's and low 60's. This information is improtant for contructing the feature image. This window is used to specify the mapping between the greyscale image intensities and the values of the feature image, which fall into the range between -1 and 1.
As soon as you change some of the parameters, the SnAP slice windows will display the feature image instead of the grey image. As you change the parameters, the slice windows are updated immideately.
If you uncheck the Preview result checkbox, the slice windows will only reflect the values of the parameters when you press the Apply button. Our goal in setting the parameters is to make sure that the voxels inside of fthe caudate nuclei are assigned positive values in the feature image, and that the voxels outside of it are assigned negative values. There are two ways to check that this happens:.
The smoothness value determines the steepness of the mapping curve. It does not affect the sign of the feature function at any particular voxel, but it does have an effect on the smoothness of the snake evolution.This section describes how SNAP can be used for manual segmentation. You will learn about working with segmentation labels, painting regions ontwo-dimensional slices of the image, and saving and loading segmentation results.
From CT to .STL: Create a Printable 3D Model from CT Scan Data: Resources
This section requires approximately 10 minutes to complete. To segment an anatomical structure in SNAP means to assign a label to each voxel in the structure. A label is a number between 0 and Associated with each label is a name and a set of display settings, such as the color used to display the label.
For example, we can associate the name 'caudate' and the color red with the label 3. When you first load a grey image into SNAP, the special label 0 is assigned to each voxel in the image. This label is associated with the name 'Clear' and means that a pixel has not been segmented yet. Before starting segmentation, we will edit these labels, assigning them meaningful names and colors. The SNAP control panel contains a sub-panel that is used to interact with segmentation labels. It is shown below.
At the top of this sub-panel are two drop-down boxes. The first box is used to select the label that is currently used for manual and automatic segmentation. The second box is used to select the label or labels that are affected by the segmentation. We will see how to use these drop boxes a few steps below. Below the drop-boxes is located a button called 'Edit Labels This label editor can be used to modify information associated with each label and to add new labels.
The left pane of the editor lists the labels that are currently available. The right pane is used to modify the label currently selected in the list. You can change the color in which the label appears in SNAP, the name associated with the label, and the transparency of the label. If you have downloaded an image archive, as recommended in Section 2, Step 1then you can load a set of segmentation labels from a file. The file contains a dozen or so labels for describing brain anatomy.
If you open the label editor, it should look like this:. In the step, we will manually segment the caudate nucleus. This step assumes that you are working with the image MRIcrop -orig. This means that we are going to apply the label called 'caduates' to the voxels that we segment.
The 'draw over all labels' settings means that segmentation will override all labels that have already been assigned to voxels. The polygon tool is used to paint closed polygons on top of the axial, sagittal and coronal slice windows. These polygons are then filled with the currently selected label. By painting polygons slice by slice, a whole 3D structure can be selected. The green rectangle around the polygon indicates that all of the vertices are currently selected.
Selected vertices can be moved by clicking and dragging the left mouse button. Let's select some vertices and move them:. In addition to selecting vertices and moving them, you can use the buttons located under each slice window to manipulate the polygon.With machines handling important tasks even in medicine, needless to say that dedicated files help doctors analyze and possibly identify malfunctions in patients organisms.
The first striking feature is the intuitive design, which provides a clever view of the imported file in an interactive preview section split into four panels.
What's more, all tools you work with are stored in a side panel and can be accessed with a single click, be it drawing, viewing or managing layers.
Simply dragging the target file over the main window is enough to get it ready for processing. Multiple layer can be created, depending on your intentions and the result you wish to obtain. Several brushes can be used to emphasize certain areas, with changes updating in real time. Navigation is a plus, with simple controls that are triggered by mouse buttons and the possibility to put only certain points of view under the spotlight. All areas and objects you create are stored in lists that are at your fingertips.
You mostly work with segments through which you emphasize elements of interest. Exporting is a flexible option, with the possibility to save using several image formats and series, either axial, coronal and sagittal.
Colors also play an important role, with a thorough editor that lets you set e distinctive sign for all areas and layers you create or manage. These can also be attributed labels, with the possibility to save reports to file.
To sum it up, ITK-SNAP is a suitable application that can be used by students and doctors alike in order to analyze medical records, with visual editing tools providing a certain degree of specialized communication. The well-organized interface is sure to get you quickly up and running, and support for various formats allow you to process nearly any file of interest. Import, analyze and modify 2D and 3D medial image files with various viewing and editing tools included in this practical application.
In addition, many bugs have been fixed. Read the full changelog. Load comments. All rights reserved.