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 | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 937-941 |
| Number of pages | 5 |
| ISBN (Print) | 9781479957514 |
| DOIs | |
| Publication status | Published - Jan 28 2014 |
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Keywords
- anomaly detection
- image segmentation
- Medical diagnostic imaging
- positron emission tomography
- tumors
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Automatic method for tumor segmentation from 3-points dynamic PET acquisitions. / Verdoja, Francesco; Grangetto, Marco; Bracco, Christian; Varetto, Teresio; Racca, Manuela; Stasi, Michele.
2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 937-941 7025188.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 - http://www.scopus.com/inward/record.url?scp=84949926795&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949926795&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2014.7025188
DO - 10.1109/ICIP.2014.7025188
M3 - Conference contribution
AN - SCOPUS:84949926795
SN - 9781479957514
SP - 937
EP - 941
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -