Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

Xiaohong Gao; Stephen Taylor; Wei Pang; Xin Lu; Barbara Braden

doi:10.1117/12.2611409

Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

Xiaohong Gao, Stephen Taylor, Wei Pang, Xin Lu, Barbara Braden

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

1 Citation (Scopus)

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Abstract

While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference ( †E) measured using CIEL∗a∗b∗ increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work.

Original language	English
Title of host publication	Medical Imaging 2022
Subtitle of host publication	Computer-Aided Diagnosis
Editors	Karen Drukker, Khan M. Iftekharuddin
Publisher	SPIE
ISBN (Electronic)	9781510649422
ISBN (Print)	9781510649415
DOIs	https://doi.org/10.1117/12.2611409
Publication status	Published - 4 Apr 2022
Event	SPIE Medical Imaging 2022 - Virtual, Online Duration: 21 Mar 2022 → 27 Mar 2022

Publication series

Name	Proceedings of SPIE
Volume	12033
ISSN (Print)	1605-7422
ISSN (Electronic)	2410-9045

Conference

Conference	SPIE Medical Imaging 2022
City	Virtual, Online
Period	21/03/22 → 27/03/22

Keywords

colour contrast
data augmentation
deep learning
Oesophageal cancer

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Biomaterials
Radiology Nuclear Medicine and imaging

UN SDGs

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

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10.1117/12.2611409

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Copyright 2022 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203303 (2022)
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Cite this

@inproceedings{7f63725ded174960801472ee16ece926,

title = "Early detection of oesophageal cancer through colour contrast enhancement for data augmentation",

abstract = "While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference ( †E) measured using CIEL∗a∗b∗ increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work. ",

keywords = "colour contrast, data augmentation, deep learning, Oesophageal cancer",

author = "Xiaohong Gao and Stephen Taylor and Wei Pang and Xin Lu and Barbara Braden",

note = "Funding Information: This work was supported by Cancer Research UK [C ref./A 29021] and by the Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, and the Oxford Biomedical Research Centre. Their financial support is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2022 SPIE.; SPIE Medical Imaging 2022 ; Conference date: 21-03-2022 Through 27-03-2022",

year = "2022",

month = apr,

day = "4",

doi = "10.1117/12.2611409",

language = "English",

isbn = "9781510649415",

series = "Proceedings of SPIE",

publisher = "SPIE",

editor = "Karen Drukker and Iftekharuddin, {Khan M.}",

booktitle = "Medical Imaging 2022",

address = "United States",

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Gao, X, Taylor, S, Pang, W, Lu, X & Braden, B 2022, Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. in K Drukker & KM Iftekharuddin (eds), Medical Imaging 2022: Computer-Aided Diagnosis., 1203303, Proceedings of SPIE, vol. 12033, SPIE, SPIE Medical Imaging 2022, Virtual, Online, 21/03/22. https://doi.org/10.1117/12.2611409

Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. / Gao, Xiaohong; Taylor, Stephen; Pang, Wei et al.
Medical Imaging 2022: Computer-Aided Diagnosis. ed. / Karen Drukker; Khan M. Iftekharuddin. SPIE, 2022. 1203303 (Proceedings of SPIE; Vol. 12033).

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

TY - GEN

T1 - Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

AU - Gao, Xiaohong

AU - Taylor, Stephen

AU - Pang, Wei

AU - Lu, Xin

AU - Braden, Barbara

N1 - Funding Information: This work was supported by Cancer Research UK [C ref./A 29021] and by the Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, and the Oxford Biomedical Research Centre. Their financial support is gratefully acknowledged. Publisher Copyright: © 2022 SPIE.

PY - 2022/4/4

Y1 - 2022/4/4

N2 - While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference ( †E) measured using CIEL∗a∗b∗ increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work.

AB - While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference ( †E) measured using CIEL∗a∗b∗ increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work.

KW - colour contrast

KW - data augmentation

KW - deep learning

KW - Oesophageal cancer

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U2 - 10.1117/12.2611409

DO - 10.1117/12.2611409

M3 - Conference contribution

AN - SCOPUS:85132843152

SN - 9781510649415

T3 - Proceedings of SPIE

BT - Medical Imaging 2022

A2 - Drukker, Karen

A2 - Iftekharuddin, Khan M.

PB - SPIE

T2 - SPIE Medical Imaging 2022

Y2 - 21 March 2022 through 27 March 2022

ER -

Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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