ITK-SNAP is a software used to segment structures in 3D medical images. It is a tool for segmenting anatomical structures in medical images. It provides an automatic active contour segmentation pipeline, along with supporting manual segmentation toolbox. ITK-SNAP has a full-featured UI aimed at clinical researchers.

SNAP provides a set of tools to make segmentation of volumetric data easier and faster. SNAP can be used in two different modes: manual segmetnation and semi-automatic segmentation. The manual mode is used for segmentation using hand contouring and for cleaning up the results of automatic segmentation. In the semi-automatic mode, a powerful level set segmentation algorithm is used to segment anatomical structures in three dimensions. This algorithm requires some guidance from the user, and SNAP provides an easy interface to provide such guidance.

Some of the core advantages of SNAP include:
1. Linked cursor for seamless 3D navigation
2. Manual segmentation in three orthogonal planes at once
3. Friendly UI for selecting active contour segmentation parameters
4. Support for many different 3D image formats, including NIfTI
5. Support for concurrent, linked viewing and segmentation of multiple images
6. Limited support for color images (e.g., diffusion tensor maps)
7. 3D cut-plane tool for fast post-processing of segmentation results
8. Extensive tutorial

First and foremost, SNAP was designed for clinical users. A user who already uses a computer for image segmentation, and thus understands the fundamentals of three-dimensional medical imaging will be able to use SNAP after completing this tutorial. SNAP does not require a deep understanding of the underlying mathematics and computer science to use.

SNAP can be used to segment a variety of three-dimensional images. The images have to be homogeneous, i.e., having a single intensity value per pixel. In other words, SNAP can be used with MRI, CT and PET images, but not with color cryosection or diffusion tensor images. SNAP reads a variety of image formats, including RAW, Analyze, GIPL and MetaImage.

SNAP represents segmentation by assigning labels to pixels (voxels) in the input image. For instance, when segmenting a brain MRI, some of the pixels in the image may be assigned the label 'grey matter', others will be assigned the label 'lateral vetricle', etc. It is up to the user to come up with the list of labels to use in a particular segmentation task. Each voxel in the input image can only be assigned a single label. The output of SNAP is a volumetric image of labels.

Since SNAP can only assign a single label to each pixel in the grey image, it can not be used for segmentation with sub-voxel accuracy.

The license of this software is Freeware, you can free download and free use this 3d graphic software.