Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis

Jiyu Sheng; Yan Li; Guitao Cao; Kai Hou

doi:10.1109/IJCNN52387.2021.9534163

Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis

Jiyu Sheng
, Yan Li
, Guitao Cao^*
, Kai Hou

^*Corresponding author for this work

Software Engineering Institute

East China Normal University
Zhongshan Hospital

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

9 Scopus citations

Abstract

Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.

Original language	English
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9534163
State	Published - 18 Jul 2021
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Online, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Online
Period	18/07/21 → 22/07/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

CT
convolutional network
follow-up
lung nodule
prediction

Access to Document

10.1109/IJCNN52387.2021.9534163

Cite this

Sheng, J., Li, Y., Cao, G., & Hou, K. (2021). Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9534163

@inproceedings{62afcfe187b54d4c8ed195aeb046a2b9,

title = "Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis",

abstract = "Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.",

keywords = "CT, convolutional network, follow-up, lung nodule, prediction",

author = "Jiyu Sheng and Yan Li and Guitao Cao and Kai Hou",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",

year = "2021",

month = jul,

day = "18",

doi = "10.1109/IJCNN52387.2021.9534163",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",

address = "美国",

}

Sheng, J, Li, Y, Cao, G & Hou, K 2021, Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Online, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9534163

Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis. / Sheng, Jiyu; Li, Yan; Cao, Guitao et al.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

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

TY - GEN

T1 - Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis

AU - Sheng, Jiyu

AU - Li, Yan

AU - Cao, Guitao

AU - Hou, Kai

PY - 2021/7/18

Y1 - 2021/7/18

N2 - Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.

AB - Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.

KW - CT

KW - convolutional network

KW - follow-up

KW - lung nodule

KW - prediction

UR - https://www.scopus.com/pages/publications/85116473121

U2 - 10.1109/IJCNN52387.2021.9534163

DO - 10.1109/IJCNN52387.2021.9534163

M3 - 会议稿件

AN - SCOPUS:85116473121

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021

Y2 - 18 July 2021 through 22 July 2021

ER -

Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis

Abstract

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UN SDGs

Keywords

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