Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models

M.C.A. van Grinsven; Thom Scheeve; Ramon-Michel Schreuder; Fons van der Sommen; Erik J. Schoon; Peter de With

doi:10.1109/ICIP.2019.8803822

Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models

M.C.A. van Grinsven, Thom Scheeve, Ramon-Michel Schreuder, Fons van der Sommen, Erik J. Schoon, Peter de With

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

3 Citaten (Scopus)

Samenvatting

Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87-0.95 and accuracies of 78-89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87-93% vs 86 87%). Direct classification predicts more premalignant polyps-as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.

Originele taal-2	Engels
Titel	2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	210-214
Aantal pagina's	5
ISBN van elektronische versie	978-1-5386-6249-6
ISBN van geprinte versie	978-1-5386-6250-2
DOI's	https://doi.org/10.1109/ICIP.2019.8803822
Status	Gepubliceerd - 26 aug. 2019
Evenement	26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan Duur: 22 sep. 2019 → 25 sep. 2019

Congres

Congres	26th IEEE International Conference on Image Processing, ICIP 2019
Land/Regio	Taiwan
Stad	Taipei
Periode	22/09/19 → 25/09/19

Bibliografische nota

Publisher Copyright:
© 2019 IEEE.

Toegang tot document

10.1109/ICIP.2019.8803822

Andere bestanden en links

Link naar publicatie in Scopus

1 Aangemelde presentatie

Image features for automated colorectal polyp classification based on clinical prediction models
Scheeve, T. (Spreker)
23 sep. 2019
Activiteit: Types gesprekken of presentaties › Aangemelde presentatie › Wetenschappelijk

3 Citaties
1 Conferentiebijdrage

Computer-aided classification of colorectal polyps using blue-light and linked-color imaging
Scheeve, T., Schreuder, R.-M., van der Sommen, F., IJspeert, J. E. G., Dekker, E., Schoon, E. J. & de With, P. H. N., 13 mrt. 2019, Medical Imaging 2019: Computer-Aided Diagnosis: Computer-Aided Diagnosis. Mori, K. & Hahn, H. K. (uitgave). Bellingham: SPIE, 8 blz. 1095012. (Proceedings of SPIE; vol. 10950).
Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review
2 Citaten (Scopus)

Citeer dit

van Grinsven, M. C. A., Scheeve, T., Schreuder, R.-M., van der Sommen, F., Schoon, E. J., & de With, P. (2019). Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (blz. 210-214). Artikel 8803822 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2019.8803822

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title = "Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models",

abstract = "Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87-0.95 and accuracies of 78-89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87-93% vs 86 87%). Direct classification predicts more premalignant polyps-as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.",

keywords = "Colorectal polyps, Quantitative features, Coputer-aided diagnosis, SVM, Gastroenterology, quantitative features, gastroenterology, computer-aided diagnosis",

author = "{van Grinsven}, M.C.A. and Thom Scheeve and Ramon-Michel Schreuder and {van der Sommen}, Fons and Schoon, {Erik J.} and {de With}, Peter",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",

year = "2019",

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van Grinsven, MCA, Scheeve, T, Schreuder, R-M, van der Sommen, F, Schoon, EJ & de With, P 2019, Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models. in 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings., 8803822, Institute of Electrical and Electronics Engineers, Piscataway, blz. 210-214, 26th IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, 22/09/19. https://doi.org/10.1109/ICIP.2019.8803822

Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models. / van Grinsven, M.C.A.; Scheeve, Thom; Schreuder, Ramon-Michel et al.
2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers, 2019. blz. 210-214 8803822.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - van Grinsven, M.C.A.

AU - Scheeve, Thom

AU - Schreuder, Ramon-Michel

AU - van der Sommen, Fons

AU - Schoon, Erik J.

AU - de With, Peter

PY - 2019/8/26

Y1 - 2019/8/26

N2 - Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87-0.95 and accuracies of 78-89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87-93% vs 86 87%). Direct classification predicts more premalignant polyps-as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.

AB - Accurate endoscopic differentiation on resection of colorectal polyps (CRPs) (resect-discard or diagnose-leave strategies) increases cost-efficiency and reduces patient risk. We aim to develop a classification algorithm for automated differentiation of CRPs, by following the validated clinical Work-group serrAted polypS and Polyposis (WASP) classification scheme. Quantitative image features are investigated for each individual WASP criterion and classification is performed by conventional SVM. The technical WASP model results in areas under the curve of 0.87-0.95 and accuracies of 78-89%. Predicting polyp histology using model-based learning out-performs medical experts (accuracy, 87-93% vs 86 87%). Direct classification predicts more premalignant polyps-as being benign, compared to the automated WASP scheme. These errors do not occur when including ROC characteristics to the WASP model. The proposed WASP model is the first automated system, competing with medical expert classification.

KW - Colorectal polyps

KW - Quantitative features

KW - Coputer-aided diagnosis

KW - SVM

KW - Gastroenterology

KW - quantitative features

KW - gastroenterology

KW - computer-aided diagnosis

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BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 26th IEEE International Conference on Image Processing, ICIP 2019

Y2 - 22 September 2019 through 25 September 2019

ER -

van Grinsven MCA, Scheeve T, Schreuder RM, van der Sommen F, Schoon EJ, de With P. Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 210-214. 8803822 doi: 10.1109/ICIP.2019.8803822

Image Features for Automated Colorectal Polyp Classification Based on Clinical Prediction Models

Samenvatting

Congres

Bibliografische nota

Toegang tot document

Andere bestanden en links

Vingerafdruk

Activiteiten

Image features for automated colorectal polyp classification based on clinical prediction models

Onderzoekersoutput

Computer-aided classification of colorectal polyps using blue-light and linked-color imaging

Citeer dit