Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods

Pedro N. Figueiredo; Isabel N. Figueiredo; Luís Pinto; Sunil Kumar; Yen-Hsi Richard Tsai; Alexander V. Mamonov

doi:10.1055/a-0808-4456

Endoscopy International Open, Table of Contents

CC BY-NC-ND 4.0 · Endosc Int Open 2019; 07(02): E209-E215
DOI: 10.1055/a-0808-4456

Original article

Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods

Pedro N. Figueiredo

¹Department of Gastroenterology, Centro Hospitalar e Universitário de Coimbra and Faculty of Medicine, University of Coimbra, Coimbra, Portugal and Centro Cirúrgico de Coimbra, Coimbra, Portugal

,

Isabel N. Figueiredo

²CMUC, Department of Mathematics, University of Coimbra, Coimbra, Portugal.

,

Luís Pinto

²CMUC, Department of Mathematics, University of Coimbra, Coimbra, Portugal.

,

Sunil Kumar

³Department of Mathematical Sciences, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, India

,

Yen-Hsi Richard Tsai

⁴Department of Mathematics and the Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States

,

Alexander V. Mamonov

⁵Department of Mathematics, University of Houston, Houston, Texas, United States

› Author Affiliations

Abstract

Background and study aims Detection of polyps during colonoscopy is essential for screening colorectal cancer and computer-aided-diagnosis (CAD) could be helpful for this objective. The goal of this study was to assess the efficacy of CAD in detection of polyps in video colonoscopy by using three methods we have proposed and applied for diagnosis of polyps in wireless capsule colonoscopy.

Patients and methods Forty-two patients were included in the study, each one bearing one polyp. A dataset was generated with a total of 1680 polyp instances and 1360 frames of normal mucosa. We used three methods, that are all binary classifiers, labelling a frame as either containing a polyp or not. Two of the methods (Methods 1 and 2) are threshold-based and address the problem of polyp detection (i. e. separation between normal mucosa frames and polyp frames) and the problem of polyp localization (i. e. the ability to locate the polyp in a frame). The third method (Method 3) belongs to the class of machine learning methods and only addresses the polyp detection problem. The mathematical techniques underlying these three methods rely on appropriate fusion of information about the shape, color and texture content of the objects presented in the medical images.

Results Regarding polyp localization, the best method is Method 1 with a sensitivity of 71.8 %. Comparing the performance of the three methods in the detection of polyps, independently of the precision in the location of the lesions, Method 3 stands out, achieving a sensitivity of 99.7 %, an accuracy of 91.1 %, and a specificity of 84.9 %.

Conclusion CAD, using the three studied methods, showed good accuracy in the detection of polyps with white light colonoscopy.

Full Text

References

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