Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion

Hélène Urien; Irène Buvat; Nicolas Rougon; Michaël Soussan; Isabelle Bloch

doi:10.1007/978-3-319-57240-6_37

Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion

Hélène Urien, Irène Buvat, Nicolas Rougon, Michaël Soussan, Isabelle Bloch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for ¹⁸F -FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET images.

Original language	English
Title of host publication	Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings
Editors	Jesus Angulo, Santiago Velasco-Forero, Fernand Meyer
Publisher	Springer Verlag
Pages	455-466
Number of pages	12
ISBN (Print)	9783319572390
DOIs	https://doi.org/10.1007/978-3-319-57240-6_37
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	13th International Symposium on Mathematical Morphology, ISMM 2017 - Fontainebleau, France Duration: 15 May 2017 → 17 May 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10225 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Symposium on Mathematical Morphology, ISMM 2017
Country/Territory	France
City	Fontainebleau
Period	15/05/17 → 17/05/17

Keywords

Brain tumors
Detection
Max-tree representation
Positron emission tomography
Spatial context

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-57240-6_37

Cite this

Urien, H., Buvat, I., Rougon, N., Soussan, M., & Bloch, I. (2017). Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. In J. Angulo, S. Velasco-Forero, & F. Meyer (Eds.), Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings (pp. 455-466). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10225 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-57240-6_37

Urien, Hélène ; Buvat, Irène ; Rougon, Nicolas et al. / Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings. editor / Jesus Angulo ; Santiago Velasco-Forero ; Fernand Meyer. Springer Verlag, 2017. pp. 455-466 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{58cda79260d5463daaca08c33f18f22b,

title = "Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion",

abstract = "In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for 18F -FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET images.",

keywords = "Brain tumors, Detection, Max-tree representation, Positron emission tomography, Spatial context",

author = "H{\'e}l{\`e}ne Urien and Ir{\`e}ne Buvat and Nicolas Rougon and Micha{\"e}l Soussan and Isabelle Bloch",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 13th International Symposium on Mathematical Morphology, ISMM 2017 ; Conference date: 15-05-2017 Through 17-05-2017",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-57240-6\_37",

language = "English",

isbn = "9783319572390",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "455--466",

editor = "Jesus Angulo and Santiago Velasco-Forero and Fernand Meyer",

booktitle = "Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings",

}

Urien, H, Buvat, I, Rougon, N, Soussan, M & Bloch, I 2017, Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. in J Angulo, S Velasco-Forero & F Meyer (eds), Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10225 LNCS, Springer Verlag, pp. 455-466, 13th International Symposium on Mathematical Morphology, ISMM 2017, Fontainebleau, France, 15/05/17. https://doi.org/10.1007/978-3-319-57240-6_37

Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. / Urien, Hélène; Buvat, Irène; Rougon, Nicolas et al.
Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings. ed. / Jesus Angulo; Santiago Velasco-Forero; Fernand Meyer. Springer Verlag, 2017. p. 455-466 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10225 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion

AU - Urien, Hélène

AU - Buvat, Irène

AU - Rougon, Nicolas

AU - Soussan, Michaël

AU - Bloch, Isabelle

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for 18F -FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET images.

AB - In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for 18F -FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET images.

KW - Brain tumors

KW - Detection

KW - Max-tree representation

KW - Positron emission tomography

KW - Spatial context

U2 - 10.1007/978-3-319-57240-6_37

DO - 10.1007/978-3-319-57240-6_37

M3 - Conference contribution

AN - SCOPUS:85019202813

SN - 9783319572390

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 455

EP - 466

BT - Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings

A2 - Angulo, Jesus

A2 - Velasco-Forero, Santiago

A2 - Meyer, Fernand

PB - Springer Verlag

T2 - 13th International Symposium on Mathematical Morphology, ISMM 2017

Y2 - 15 May 2017 through 17 May 2017

ER -

Urien H, Buvat I, Rougon N, Soussan M, Bloch I. Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion. In Angulo J, Velasco-Forero S, Meyer F, editors, Mathematical Morphology and Its Applications to Signal and Image Processing - 13th International Symposium, ISMM 2017, Proceedings. Springer Verlag. 2017. p. 455-466. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-57240-6_37

Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this