Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data.
OBJECTIVE
Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models.


METHODS
We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process.


RESULTS
The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization.


CONCLUSIONS
The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.
Böttger
Thomas
T
Division Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany. thomas.boettger@siemens.com
Kunert
Tobias
T
Meinzer
Hans P
HP
Wolf
Ivo
I
D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine