An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

Liyun Chen; Dongdong Gu; Yanbo Chen; Ying Shao; Xiaohuan Cao; Guocai Liu; Yaozong Gao; Qian Wang; Dinggang Shen

doi:10.1016/j.compmedimag.2021.101899

An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

Liyun Chen, Dongdong Gu, Yanbo Chen, Ying Shao, Xiaohuan Cao, Guocai Liu, Yaozong Gao, Qian Wang, Dinggang Shen

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

Abstract

Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

Original language	English
Article number	101899
Journal	Computerized Medical Imaging and Graphics
Volume	89
DOIs	https://doi.org/10.1016/j.compmedimag.2021.101899
Publication status	Published - 2021 Apr
Externally published	Yes

Bibliographical note

Funding Information:
This work was partially supported by the National Key Research and Development Program of China ( 2018YFC0116400 ), the Science and Technology Commission of Shanghai Municipality ( 19QC1400600 ) and the National Natural Science Foundation of China ( 62071176 ).

Publisher Copyright:
© 2021

Keywords

Computed tomography (CT)
Lung nodule atlas
Lung nodule detection
Lung nodule segmentation
Statistical analysis

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

UN SDGs

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

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10.1016/j.compmedimag.2021.101899

Cite this

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title = "An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans",

abstract = "Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.",

keywords = "Computed tomography (CT), Lung nodule atlas, Lung nodule detection, Lung nodule segmentation, Statistical analysis",

author = "Liyun Chen and Dongdong Gu and Yanbo Chen and Ying Shao and Xiaohuan Cao and Guocai Liu and Yaozong Gao and Qian Wang and Dinggang Shen",

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doi = "10.1016/j.compmedimag.2021.101899",

language = "English",

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T1 - An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

AU - Chen, Liyun

AU - Gu, Dongdong

AU - Chen, Yanbo

AU - Shao, Ying

AU - Cao, Xiaohuan

AU - Liu, Guocai

AU - Gao, Yaozong

AU - Wang, Qian

AU - Shen, Dinggang

N1 - Funding Information: This work was partially supported by the National Key Research and Development Program of China ( 2018YFC0116400 ), the Science and Technology Commission of Shanghai Municipality ( 19QC1400600 ) and the National Natural Science Foundation of China ( 62071176 ). Publisher Copyright: © 2021

PY - 2021/4

Y1 - 2021/4

N2 - Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

AB - Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

KW - Computed tomography (CT)

KW - Lung nodule atlas

KW - Lung nodule detection

KW - Lung nodule segmentation

KW - Statistical analysis

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AN - SCOPUS:85102842850

SN - 0895-6111

VL - 89

JO - Computerized Medical Imaging and Graphics

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M1 - 101899

ER -

An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this