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

Francesco Verdoja; Marco Grangetto; Christian Bracco; Teresio Varetto; Manuela Racca; Michele Stasi

doi:10.1109/ICIP.2014.7025188

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

Francesco Verdoja
, Marco Grangetto
, Christian Bracco
, Teresio Varetto
, Manuela Racca
, Michele Stasi

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

Abstract

In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative 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 preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.

Original language	English
Title of host publication	2014 IEEE International Conference on Image Processing, ICIP 2014
Publisher	IEEE
Pages	937-941
Number of pages	5
ISBN (Electronic)	9781479957514
DOIs	https://doi.org/10.1109/ICIP.2014.7025188
Publication status	Published - 28 Jan 2014
MoE publication type	A4 Conference publication

Keywords

anomaly detection
image segmentation
Medical diagnostic imaging
positron emission tomography
tumors

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10.1109/ICIP.2014.7025188

Cite this

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title = "Automatic method for tumor segmentation from 3-points dynamic PET acquisitions",

abstract = "In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative 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 preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.",

keywords = "anomaly detection, image segmentation, Medical diagnostic imaging, positron emission tomography, tumors",

author = "Francesco Verdoja and Marco Grangetto and Christian Bracco and Teresio Varetto and Manuela Racca and Michele Stasi",

year = "2014",

month = jan,

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doi = "10.1109/ICIP.2014.7025188",

language = "English",

pages = "937--941",

booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",

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Automatic method for tumor segmentation from 3-points dynamic PET acquisitions. / Verdoja, Francesco; Grangetto, Marco; Bracco, Christian et al.
2014 IEEE International Conference on Image Processing, ICIP 2014. IEEE, 2014. p. 937-941 7025188.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Automatic method for tumor segmentation from 3-points dynamic PET acquisitions

AU - Verdoja, Francesco

AU - Grangetto, Marco

AU - Bracco, Christian

AU - Varetto, Teresio

AU - Racca, Manuela

AU - Stasi, Michele

PY - 2014/1/28

Y1 - 2014/1/28

N2 - In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative 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 preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.

AB - In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative 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 preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.

KW - anomaly detection

KW - image segmentation

KW - Medical diagnostic imaging

KW - positron emission tomography

KW - tumors

UR - https://www.scopus.com/pages/publications/84949926795

U2 - 10.1109/ICIP.2014.7025188

DO - 10.1109/ICIP.2014.7025188

M3 - Conference article in proceedings

AN - SCOPUS:84949926795

SP - 937

EP - 941

BT - 2014 IEEE International Conference on Image Processing, ICIP 2014

PB - IEEE

ER -

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

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Keywords

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