Automatic volume delineation in oncological PET

F. Hofheinz; C. Pötzsch; L. Oehme; B. Beuthien-Baumann; J. Steinbach; J. Kotzerke; J. van den Hoff

doi:10.3413/Nukmed-0419-11-07

Nuklearmedizin - NuclearMedicine, Table of Contents

Nuklearmedizin 2012; 51(01): 9-16
DOI: 10.3413/Nukmed-0419-11-07

Original article

Schattauer GmbH

Automatic volume delineation in oncological PET

Evaluation of a dedicated software tool and comparison with manual delineation in clinical data setsAutomatische Volumenabgrenzung in der onko logischen PETBewertung eines entsprechenden Software-Werkzeugs und Vergleich mit manueller Abgrenzung anhand klinischer Datensätze

Authors

F. Hofheinz

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany
C. Pötzsch

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany
L. Oehme

²Department of Nuclear Medicine, University Hospital Carl Gustav Carus at the Dresden University of Technology, Dresden, Germany
B. Beuthien-Baumann

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany

²Department of Nuclear Medicine, University Hospital Carl Gustav Carus at the Dresden University of Technology, Dresden, Germany
J. Steinbach

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany
J. Kotzerke

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany

²Department of Nuclear Medicine, University Hospital Carl Gustav Carus at the Dresden University of Technology, Dresden, Germany
J. van den Hoff

¹PET Center, Institute of Radiopharmacy, Helmholtz-Zentrum Dresden-Rossendorf, Germany

²Department of Nuclear Medicine, University Hospital Carl Gustav Carus at the Dresden University of Technology, Dresden, Germany

Abstract

Summary

Aim: Evaluation of a dedicated software tool for automatic delineation of 3D regions of interest in oncological PET. Patients, methods: The applied procedure encompasses segmentation of user-specified subvolumes within the tomographic data set into separate 3D ROIs, automatic background determination, and local adaptive thresholding of the background corrected data. Background correction and adaptive thresholding are combined in an iterative algorithm. Nine experienced observers used this algorithm for automatic delineation of a total of 37 ROIs in 14 patients. Additionally, the observers delineated the same ROIs also manually (using a freely chosen threshold for each ROI) and the results of automatic and manual ROI delineation were compared. Results: For the investigated 37 ROIs the manual delineation shows a strong interobserver variability of (26.8 ± 6.3)% (range: 15% to 45%) while the corresponding value for automatic delineation is (1.1 ± 1.0)% (range: <0.1% to 3.6%). The fractional deviation of the automatic volumes from the observer-averaged manual ones is (3.7 ± 12.7)%. Conclusion: The evaluated software provides results in very good agreement with observer-averaged manual evaluations, facilitates and accelerates the volumetric evaluation, eliminates the problem of interobserver variability and appears to be a useful tool for volumetric evaluation of oncological PET in clinical routine.

Zusammenfassung

Ziel: Evaluierung eines für die automatische Abgrenzung von 3D-ROIs bestimmten Verfahrens in der onkologischen PET. Patienten, Methoden: Das Verfahren umfasst die Segmentierung benutzerdefinierter Teilvolumina innerhalb des tomographischen Datensatzes in getrennte 3D-ROIs, automatische Untergrundbestimmung und die Anwendung eines lokaladaptiven Schwellwertverfahrens auf die untergrundkorrigierten Bilddaten. Untergrundkorrektur und Schwellwertanalyse sind hierbei Teil eines iterativen Algorithmus. Neun erfahrene Personen nutzten diesen Algorithmus zur automatischen Abgrenzung von 37 ROIs bei 14 Patienten. Zusätzlich grenzte jeder Nutzer die gleichen Strukturen manuell ab (mit frei wählbarem Schwellwert für jede ROI). Die Resultate der automatischen und manuellen ROI-Abgrenzung wurden verglichen. Resultate: Für die untersuchten 37 ROIs zeigt die manuelle Auswertung eine starke Interobserver- Variabilität von (26.8 ± 6.3)% (Spannbreite: 15% bis 45%), der entsprechende Wert für die automatische Abgrenzung beträgt (1.1 ± 1.0)% (Spannbreite: 0.1% bis 3.6%). Die prozentuale Abweichung der automatisch bestimmten Volumina von den manuell bestimmten ist (3.7 ± 12.7)%. Schlussfolgerung: Die untersuchte Software liefert Resultate, die sehr gut mit der nutzergemittelten manuellen Auswertung überein stimmen. Sie erleichtert und beschleunigt die volumetrische Auswertung, eliminiert das Problem der Interobserver- Variabilität und scheint ein nützliches Werkzeug für die volumetrische Auswertung der onkologischen PET in der klinischen Routine zu sein.

Keywords

3D ROI quantification - PET - volume delineation - background correction - interobserver variability

Schlüsselwörter

3D-ROI - quantitative PET - Volumenabgrenzung - Untergrundkorrektur - Interobservervariabilität

Full Text

References

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