Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures

Adrien Meyer; Antoine Fleurentin; Julieta Montanelli; Jean Paul Mazellier; Lee Swanstrom; Benoit Gallix; Georgios Exarchakis; Leonardo Sosa Valencia; Nicolas Padoy

doi:10.1007/978-3-031-16902-1_2

Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures

Adrien Meyer, Antoine Fleurentin, Julieta Montanelli, Jean Paul Mazellier, Lee Swanstrom, Benoit Gallix, Georgios Exarchakis, Leonardo Sosa Valencia, Nicolas Padoy

Department of Computer Science

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

1 Citation (SciVal)

Abstract

Current 3D imaging techniques (computed tomography scan, magnetic resonance imaging) offer poor detection of early-stage pancreatic cancers, which in turn leads to high mortality rates. Endoscopic ultrasound (EUS) is a proven alternative to increase early diagnosis and identify potentially curable surgery candidates. However, mastering EUS requires a lot of practice to properly navigate and interpret video flow. Real time computer assisted localization of anatomical structures could improve lesion detection while easing the overall procedure by pointing out anatomical landmarks otherwise complex to identify. For this purpose, we propose a novel architecture built on top of object detection literature by combining spatial attention and temporal information. In parallel, we have created EUS-D50, a representative EUS dataset constituted of 50 EUS pancreas videos with spatial annotations including pancreas parenchyma and lesions. On EUS-D50, our work achieve an mAP@50 of 58.36 % for pancreatic parenchyma and lesion.

Original language	English
Title of host publication	Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings
Editors	Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	13-22
Number of pages	10
ISBN (Print)	9783031169014
DOIs	https://doi.org/10.1007/978-3-031-16902-1_2
Publication status	Published - 15 Sept 2022
Event	3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sept 2022 → 18 Sept 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13565 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 18/09/22

Bibliographical note

Funding Information:
Acknowledgement. This work was carried out within the framework of the project APEUS supported by the ARC Foundation (www.fondation-arc.org) and was partially supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (reference ANR-10-IAHU-02).

Funding

Acknowledgement. This work was carried out within the framework of the project APEUS supported by the ARC Foundation (www.fondation-arc.org) and was partially supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (reference ANR-10-IAHU-02).

Keywords

Deep learning
Endoscopic ultrasound
Pancreas
Video object detection

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-031-16902-1_2

Cite this

Meyer, A., Fleurentin, A., Montanelli, J., Mazellier, J. P., Swanstrom, L., Gallix, B., Exarchakis, G., Sosa Valencia, L., & Padoy, N. (2022). Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures. In S. Aylward, J. A. Noble, Y. Hu, S.-L. Lee, Z. Baum, & Z. Min (Eds.), Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings (pp. 13-22). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13565 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16902-1_2

Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures. / Meyer, Adrien; Fleurentin, Antoine; Montanelli, Julieta et al.
Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings. ed. / Stephen Aylward; J. Alison Noble; Yipeng Hu; Su-Lin Lee; Zachary Baum; Zhe Min. Springer Science and Business Media Deutschland GmbH, 2022. p. 13-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13565 LNCS).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Meyer, A, Fleurentin, A, Montanelli, J, Mazellier, JP, Swanstrom, L, Gallix, B, Exarchakis, G, Sosa Valencia, L & Padoy, N 2022, Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures. in S Aylward, JA Noble, Y Hu, S-L Lee, Z Baum & Z Min (eds), Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13565 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 13-22, 3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-16902-1_2

Meyer A, Fleurentin A, Montanelli J, Mazellier JP, Swanstrom L, Gallix B et al. Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures. In Aylward S, Noble JA, Hu Y, Lee SL, Baum Z, Min Z, editors, Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 13-22. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16902-1_2

Meyer, Adrien ; Fleurentin, Antoine ; Montanelli, Julieta et al. / Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures. Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings. editor / Stephen Aylward ; J. Alison Noble ; Yipeng Hu ; Su-Lin Lee ; Zachary Baum ; Zhe Min. Springer Science and Business Media Deutschland GmbH, 2022. pp. 13-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{37312b8a7ae74267b1e4c97871cec561,

title = "Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures",

abstract = "Current 3D imaging techniques (computed tomography scan, magnetic resonance imaging) offer poor detection of early-stage pancreatic cancers, which in turn leads to high mortality rates. Endoscopic ultrasound (EUS) is a proven alternative to increase early diagnosis and identify potentially curable surgery candidates. However, mastering EUS requires a lot of practice to properly navigate and interpret video flow. Real time computer assisted localization of anatomical structures could improve lesion detection while easing the overall procedure by pointing out anatomical landmarks otherwise complex to identify. For this purpose, we propose a novel architecture built on top of object detection literature by combining spatial attention and temporal information. In parallel, we have created EUS-D50, a representative EUS dataset constituted of 50 EUS pancreas videos with spatial annotations including pancreas parenchyma and lesions. On EUS-D50, our work achieve an mAP@50 of 58.36 % for pancreatic parenchyma and lesion.",

keywords = "Deep learning, Endoscopic ultrasound, Pancreas, Video object detection",

author = "Adrien Meyer and Antoine Fleurentin and Julieta Montanelli and Mazellier, {Jean Paul} and Lee Swanstrom and Benoit Gallix and Georgios Exarchakis and {Sosa Valencia}, Leonardo and Nicolas Padoy",

note = "Funding Information: Acknowledgement. This work was carried out within the framework of the project APEUS supported by the ARC Foundation (www.fondation-arc.org) and was partially supported by French state funds managed within the “Plan Investissements d{\textquoteright}Avenir” and by the ANR (reference ANR-10-IAHU-02). ; 3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",

year = "2022",

month = sep,

day = "15",

doi = "10.1007/978-3-031-16902-1_2",

language = "English",

isbn = "9783031169014",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "13--22",

editor = "Stephen Aylward and Noble, {J. Alison} and Yipeng Hu and Su-Lin Lee and Zachary Baum and Zhe Min",

booktitle = "Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings",

address = "Germany",

}

TY - GEN

T1 - Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures

AU - Meyer, Adrien

AU - Fleurentin, Antoine

AU - Montanelli, Julieta

AU - Mazellier, Jean Paul

AU - Swanstrom, Lee

AU - Gallix, Benoit

AU - Exarchakis, Georgios

AU - Sosa Valencia, Leonardo

AU - Padoy, Nicolas

N1 - Funding Information: Acknowledgement. This work was carried out within the framework of the project APEUS supported by the ARC Foundation (www.fondation-arc.org) and was partially supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (reference ANR-10-IAHU-02).

PY - 2022/9/15

Y1 - 2022/9/15

N2 - Current 3D imaging techniques (computed tomography scan, magnetic resonance imaging) offer poor detection of early-stage pancreatic cancers, which in turn leads to high mortality rates. Endoscopic ultrasound (EUS) is a proven alternative to increase early diagnosis and identify potentially curable surgery candidates. However, mastering EUS requires a lot of practice to properly navigate and interpret video flow. Real time computer assisted localization of anatomical structures could improve lesion detection while easing the overall procedure by pointing out anatomical landmarks otherwise complex to identify. For this purpose, we propose a novel architecture built on top of object detection literature by combining spatial attention and temporal information. In parallel, we have created EUS-D50, a representative EUS dataset constituted of 50 EUS pancreas videos with spatial annotations including pancreas parenchyma and lesions. On EUS-D50, our work achieve an mAP@50 of 58.36 % for pancreatic parenchyma and lesion.

AB - Current 3D imaging techniques (computed tomography scan, magnetic resonance imaging) offer poor detection of early-stage pancreatic cancers, which in turn leads to high mortality rates. Endoscopic ultrasound (EUS) is a proven alternative to increase early diagnosis and identify potentially curable surgery candidates. However, mastering EUS requires a lot of practice to properly navigate and interpret video flow. Real time computer assisted localization of anatomical structures could improve lesion detection while easing the overall procedure by pointing out anatomical landmarks otherwise complex to identify. For this purpose, we propose a novel architecture built on top of object detection literature by combining spatial attention and temporal information. In parallel, we have created EUS-D50, a representative EUS dataset constituted of 50 EUS pancreas videos with spatial annotations including pancreas parenchyma and lesions. On EUS-D50, our work achieve an mAP@50 of 58.36 % for pancreatic parenchyma and lesion.

KW - Deep learning

KW - Endoscopic ultrasound

KW - Pancreas

KW - Video object detection

UR - http://www.scopus.com/inward/record.url?scp=85138770310&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-16902-1_2

DO - 10.1007/978-3-031-16902-1_2

M3 - Chapter in a published conference proceeding

AN - SCOPUS:85138770310

SN - 9783031169014

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

SP - 13

EP - 22

BT - Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings

A2 - Aylward, Stephen

A2 - Noble, J. Alison

A2 - Hu, Yipeng

A2 - Lee, Su-Lin

A2 - Baum, Zachary

A2 - Min, Zhe

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 18 September 2022

ER -