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Mirko Arnold, Anarta Ghosh, Stephen Ameling, & G Lacey. (2010). Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP JIVP - EURASIP Journal on Image and Video Processing, 2010(9).
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Jorge Bernal. (2014). Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps. ELCVIA - Electronic Letters on Computer Vision and Image Analysis, 13(2), 9–10.
Abstract: Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians.
Keywords: Colonoscopy; polyp localization; polyp segmentation; Eye-tracking
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Gloria Fernandez Esparrach, Jorge Bernal, Maria Lopez Ceron, Henry Cordova, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, et al. (2016). Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. END - Endoscopy, 48(9), 837–842.
Abstract: Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
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Cristina Sanchez Montes, Jorge Bernal, Ana Garcia Rodriguez, Henry Cordova, & Gloria Fernandez Esparrach. (2020). Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia. GH - Gastroenterología y Hepatología, 43(4), 222–232.
Abstract: Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.
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Jorge Bernal, Nima Tajkbaksh, F. Javier Sanchez, Bogdan J. Matuszewski, Hao Chen, Lequan Yu, et al. (2017). Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge. TMI - IEEE Transactions on Medical Imaging, 36(6), 1231–1249.
Abstract: Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Keywords: Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework
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