Session 4. Imaging and image processing I – Optics and endoscopy

Bost, Wolfgang; Oeri, Milan; Weber, Steffen; Tretbar, Steffen; Fournelle, Marc

doi:10.1515/bmt-2017-5003

Cited by 1 publication

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“…Based on the abovementioned availability of texture- and structure-based features for the image-based characterization and statistical classification of tissues in the colon, the highlight of this era (and also representing its virtual end) was denoted by the first published system for real-time, low-delay polyp detection based on HD video streams during a prospective screening colonoscopy study [44, 45] (Fig. 6).…”

Section: The Age Of Statistical Learningmentioning

confidence: 99%

Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

Wittenberg

Raithel

2020

Visc Med

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Background: In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. Summary: With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. Key Messages: Researchofimage-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

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Section: The Age Of Statistical Learningmentioning

confidence: 99%