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/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

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-2Engels
Titel2019 IEEE International Conference on Image Processing (ICIP)
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's210-214
Aantal pagina's5
ISBN van elektronische versie978-1-5386-6249-6
ISBN van geprinte versie978-1-5386-6250-2
DOI's
StatusGepubliceerd - 26 aug 2019
Evenement26th IEEE International Conference on Image Processing (ICIP 2019) - Taipei, Taiwan
Duur: 22 sep 201925 sep 2019

Congres

Congres26th IEEE International Conference on Image Processing (ICIP 2019)
Verkorte titelICIP 2019
LandTaiwan
StadTaipei
Periode22/09/1925/09/19

Vingerafdruk

Histology
Costs

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) (blz. 210-214). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2019.8803822
van Grinsven, M.C.A. ; Scheeve, Thom ; Schreuder, Ramon-Michel ; van der Sommen, Fons ; Schoon, Erik J. ; de With, Peter. / Image features for automated colorectal polyp classification based on clinical prediction models. 2019 IEEE International Conference on Image Processing (ICIP). Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 210-214
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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",
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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). Institute of Electrical and Electronics Engineers, Piscataway, blz. 210-214, 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; van der Sommen, Fons; Schoon, Erik J.; de With, Peter.

2019 IEEE International Conference on Image Processing (ICIP). Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 210-214.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AU - Schreuder, Ramon-Michel

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AU - de With, Peter

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van Grinsven MCA, Scheeve T, Schreuder R-M, 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). Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 210-214 https://doi.org/10.1109/ICIP.2019.8803822