In this paper, we present a semi-automtic method for segmenting carotid arteries in contrast enhanced (CE)-CT angiography (CTA) scans. 
The segmentation algorithm extracts the lumen of carotid arteries between user specfied locations. Specifically, the algorithm first 
detects the centerline representations between the user placed seed points. This centerline extraction algorithm is based on a minimal path 
detection algorithm which operates on a {\em medialness} map. The lumen of corotid arteries is extracted by using 
the global optimal graph-cuts algorithm~\cite{boykov:01} using the centerlines as input. The radius information contained 
in the centerline representation
is used to normalize the gradient based weights of the graph. It is shown that this algorithm can sucessfully segment the carotid arteries without
including calcified and non-calcified plaques in the segmentation results.

Segmentation of Carotid Arteries By Graph-Cuts Using Centerline Models

We describe a new class itk::DicomDirectoryToImage which, given a directory consisting of raw dicom files, converts all found series to volumetric data with consistent filenaming based on the dicom headers. In addition various internal checks on the dicom files are made and reported, and the user has the possibility to add simple criteria to match with the series description. The main advantage of the class is the ease of processing and converting very large dicom databases in a consistent way.

DicomImport

This paper presents parsing software for importing contours from DICOM RT structure set files into binary image files. This is a common requirement for transferring expert manual segmentations from radiotherapy treatment planning software into external image analysis environments (such as ITK). We provide an implementation of RT structure set importing and give some example results from prostate cancer radiotherapy planning volumes. 

Please note that this implementation requires GDCM 2.0 to be installed (this is trivial; however this requirement is the reason for the low automatic testing score by the Insight-Journal Dashboard).

Please Note: Updated June 2010 for a couple of minor issues (pls. see comments in .cxx) and to remove the requirement for image template. This code now reads the dcm directory and creates a 3D volume and associated binary structure volumes (all in NIfTI format).
Usage: itkDICOMRT DicomDirectory inputDICOMRTImageFile OutputFilePrefix

Feb 2013: Please note that this code won't work with ITK4 and above - instead please see: http://hdl.handle.net/10380/3401 . There is also a new test DICOM-RT directory with that paper. 

Mar 2013 Now correctly handles holes within structures. Thanks to Sebastian Steger for the code fix.


Importing Contours from DICOM-RT Structure Sets

An Insight Toolkit (ITK) algorithm for adaptively thresholding images is presented in this paper. Currently, the usage of thresholding methods in ITK has made use of global thresholds, confidence connected thresholds and neighborhood strategies. The current work extends these family of filters by setting thresholds adaptively in local image regions. The user is not required to specify seed regions apriori which greatly eases the task of automatic segmentation. The thresholds are determined using Otsu's minimization of between-class variances in local image regions that are selected randomly throughout the domain. Using non-uniformly sampled thresholds, a continuous function is reconstructed throughout the image domain using a B-Spline approximation algorithm. Hence, the image domain is adaptively sampled by making use of the reconstructed threshold function. Most imaging modalities introduce some intensity inhomogeneities that can be recovered by this method. 

An Adaptive Thresholding Image Filter

This paper presents the results of the Head and Neck Auto-segmentationChallenge, which was part of the workshop “3D Segmentation in the Clinic: A Grand Challenge”. This workshop took place in London, UK, in September 2009, in conjunction with the 12th conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). The aim of the challenge was to evaluate the performance of fully automated algorithms in segmenting the mandible and the brainstem in head and neck CT image data used in radiotherapy planning. We describe the motivation behind the clinical application selected for the challenge, the image data used, and the metrics applied for the quantitative assessment of the segmentation accuracy with respect to the ground truth segmentations provided by a clinical expert. The quantitative evaluation results of the auto-segmentations submitted by the workshop participants are included.

Head and Neck Auto-segmentation Challenge

This document introduces a program based on the algorithm described by Maria Cristina Tugurlan. The program uses file readers, image filters, and file writers from the Insight Toolkit ITK www.itk.org. It produces as output an image whose values are the times of first arrival of a wavefront that spreads from seed points with a speed at every point equal to the input image intensity. It performs the computation in parallel on distributed memory computers using the Message Passing Interface MPI. Each MPI process reads a small piece of the input image into memory. It computes fast marching on its piece. It sends and receives the values from fast marching at piece boundaries. It recalculates fast marching a number of times set from the command line using the new boundary values each time. Each MPI process writes only a small piece of the output file.

A substantial difference is seen when the output of MPI fast marching is compared to the output of serial fast marching. This difference may be acceptable for some uses. The program should be able to handle input images that are too large to fit in the memory of a single computer.

An Implementation of Parallel Fast Marching Using the Message Passing Interface

In this submission, we offer the GaussianInterpolationImageFunction which adds to the growing collection of existing interpolation algorithms in ITK for resampling scalar images such as the LinearInterpolateImageFunction, BSplineInterpolateImageFunction, and WindowedSincInterpolateImageFunction. We provide a brief discussion of the theory behind the submission and the algorithmic implementation.

Gaussian Interpolation

This guide is intended as a very brief introduction of the main tools in the Computational Morphometry Toolkit (CMTK), which is available in source code and as precompiled binaries from http://www.nitrc.org/projects/cmtk/. The target audience of this document are CMTK users, who might use this document as a reference to the most common processing tasks, and prospective users, who may find this information useful to determine whether CMTK provides functionality that they can use. We focus in particular on a simplified workflow for deformation morphometry studies based on magnetic resonance images: DICOM conversion, artifact correction, affine and nonlinear image registration, reformatting, Jacobian determinant map generation, and statistical hypothesis testing.

User Guide to The Computational Morphometry Toolkit

Although the KappaStatisticImageToImageMetric can be used to obtain the Dice metric (or mean overlap), there are other related measurements that are useful for evaluating results derived from various image analysis tasks. These measures include the target overlap, union overlap (or Jaccard coefficient), and false positive/negative errors. There are also related measures for multilabeled images. The class included with this submission, the itkLabelOverlapMeasuresImageFilter, is meant to provide an easy mechanism for calculating such measures.

Introducing Dice, Jaccard, and Other Label Overlap Measures To ITK

This document presents a set of classes (vtkPointSetOutlierRemoval, vtkPointSetNormalEstimation, vtkPointSetNormalOrientation, vtkPointSetCurvatureEstimation, vtkEuclideanMinimumSpanningTree, and vtkRiemannianGraphFilter) to enable several basic operations on point sets. These classes are implemented as VTK filters. Paraview plugin interfaces to the filters are also provided to allow extremely easy experimentation with the new functionality. We propose these classes as an addition to the Visualization Toolkit. 

The latest source code can be found here: https://github.com/daviddoria/PointSetProcessing