Computer Vision in Esophageal Cancer: A Literature Review

Esophageal cancer is a disease with a high prevalence that can be evaluated by a variety of imaging modalities, including endoscopy, computed tomography, and positron emission tomography. Computer-aided techniques could provide a valuable help in the analysis of these images, decreasing the medical...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.103080-103094
Main Authors: Domingues, Ines, Sampaio, Ines Lucena, Duarte, Hugo, Santos, Joao A. M., Abreu, Pedro H.
Format: Article
Language:English
Subjects:
Cancer
Computed tomography
computer aided analysis
Computer vision
Emission analysis
Endoscopes
endoscopy
Esophageal cancer
Human error
Literature reviews
Magnetic resonance imaging
Medical imaging
Positron emission
positron emission tomography
Tomography
Tumors
Workflow
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Summary:Esophageal cancer is a disease with a high prevalence that can be evaluated by a variety of imaging modalities, including endoscopy, computed tomography, and positron emission tomography. Computer-aided techniques could provide a valuable help in the analysis of these images, decreasing the medical workflow time and human errors. The goal of this paper is to review the existing literature on the application of computer vision techniques in the domain of esophageal cancer. After an initial phase where a set of keywords was chosen, the selected terms were used to retrieve papers from four well-known databases: Web of Science, Scopus, PubMed, and Springer. The results were scanned by merging identical entries, and eliminating the out of scope works, resulting in 47 selected papers. These were organized according to the image modality. Major results were then summarized and compared, and main shortcomings were identified. It could be concluded that, even though the scientific community has already paid attention to the esophageal cancer problem, there are still several open issues. Two major findings of this review are the nonexistence of works on MRI data and the under-exploration of recent techniques using deep learning strategies, showing the need for further investigation.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2930891