PET image segmentation using a Gaussian mixture model and Markov random fields.
BACKGROUND
Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters.


METHODS
An improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume, followed by a correction step based on a Markov random field (MRF) and a Gibbs distribution to account for dependencies among neighboring voxels. In order to evaluate the proposed algorithm, phantom measurements of spherical and non-spherical objects with the smallest diameter being 8 mm were performed at signal-to-background ratios (SBRs) between 2.06 and 9.39. Additionally (68)Ga-PET data from patients with lesions in the liver and lymph nodes were evaluated.


RESULTS
The proposed algorithm produces stable results for different reconstruction algorithms and different lesion shapes. Furthermore, it outperforms all threshold methods regarding detection rate, determines the spheres' volumes more accurately than fixed threshold methods, and produces similar values as iterative thresholding. In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs. The comparison with experts' manual delineations on the clinical data shows the same qualitative behavior as for the phantom measurements.


CONCLUSIONS
In conclusion, a generic probabilistic approach that does not require data measured beforehand is presented whose performance, robustness, and swiftness make it a feasible choice for PET segmentation.
Institute of Telecommunications, Vienna University of Technology, Karlsplatz 13, Vienna, 1040 Wien, Austria. thomas.layer@gmx.at.|Health & Environment Department, Austrian Institute of Technology, Donau-City-Strasse 1/2, Vienna, 1220 Wien, Austria. thomas.layer@gmx.at.
Thomas
T
Layer
Health & Environment Department, Austrian Institute of Technology, Donau-City-Strasse 1/2, Vienna, 1220 Wien, Austria. matthias.blaickner@ait.ac.at.
Matthias
M
Blaickner
Department of Radiation Oncology, Division of Medical Radiation Physics, Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna/AKH Vienna, Währinger Gürtel 18-20, Vienna, 1090 Wien, Austria. barbara.knaeusl@meduniwien.ac.at.
Barbara
B
Knäusl
Department of Radiation Oncology, Division of Medical Radiation Physics, Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna/AKH Vienna, Währinger Gürtel 18-20, Vienna, 1090 Wien, Austria. dietmar.georg@meduniwien.ac.at.
Dietmar
D
Georg
Radiation Safety and Applications, Seibersdorf Labor GmbH, 2444 Seibersdorf, Seibersdorf, Austria. Johannes.Neuwirth@seibersdorf-laboratories.at.
Johannes
J
Neuwirth
THERANOSTICS Center for Molecular Radiotherapy and Molecular Imaging (PET/CT) ENETS Center of Excellence, Zentralklinik Bad Berka, Robert-Koch-Allee 9, 99437 Bad Berka, Bad Berka, Germany. richard.baum@zentralklinik.de.
Richard P
RP
Baum
THERANOSTICS Center for Molecular Radiotherapy and Molecular Imaging (PET/CT) ENETS Center of Excellence, Zentralklinik Bad Berka, Robert-Koch-Allee 9, 99437 Bad Berka, Bad Berka, Germany. Christiane.Schuchardt@zentralklinik.de.
Christiane
C
Schuchardt
THERANOSTICS Center for Molecular Radiotherapy and Molecular Imaging (PET/CT) ENETS Center of Excellence, Zentralklinik Bad Berka, Robert-Koch-Allee 9, 99437 Bad Berka, Bad Berka, Germany. Stefan.Wiessalla@zentralklinik.de.
Stefan
S
Wiessalla
Institute of Telecommunications, Vienna University of Technology, Karlsplatz 13, Vienna, 1040 Wien, Austria. gerald.matz@nt.tuwien.ac.at.
Gerald
G
Matz
Expectation maximization; Markov random field; Positron emission tomography; Radiotherapy; Tumor segmentation