In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An inno- vative anomaly detection tool tailored for 3-points dynamic PET scans is designed. The algorithm allows the identification of tumoral cells in dynamic FDG-PET scans thanks to their peculiar anaerobic metabolism experienced over time. The proposed tool is prelimi- narily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classi- fication errors.

Automatic method for tumor segmentation from 3-points dynamic PET acquisitions

VERDOJA, FRANCESCO;GRANGETTO, Marco;
2014

Abstract

In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An inno- vative anomaly detection tool tailored for 3-points dynamic PET scans is designed. The algorithm allows the identification of tumoral cells in dynamic FDG-PET scans thanks to their peculiar anaerobic metabolism experienced over time. The proposed tool is prelimi- narily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classi- fication errors.
IEEE International Conference on Image Processing
Paris, France
27-30/10/2014
IEEE International Conference on Image Processing
IEEE
937
941
9781479957507
Francesco Verdoja; Marco Grangetto;Christian Bracco; Teresio Varetto; Manuela Racca; Michele Stasi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/154166
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