2017
DOI: 10.1590/2446-4740.03316
|View full text |Cite
|
Sign up to set email alerts
|

Approaches to segment stent area from Intravascular Optical Coherence Tomography

Abstract: Introduction: Cardiovascular diseases (CVD) have been the focus of research in recent years due to its high mortality rate. It is estimated that 17.5 million people died of CVD in 2012, from which 7.4 million were due to coronary heart disease (CHD). In order to monitor CHD patients and avoid waste of specialists' time, this study proposes the development of a method that segments the area contained by stent struts from Frequency Domain Intravascular Optical Coherence Tomography (the latest technology to view … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2018
2018
2019
2019

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 13 publications
0
2
0
Order By: Relevance
“…For OCT research, manual segmentation has remained the gold-standard to extract structural information of the ONH, and this is especially true for deeper connective tissues [8,9]. However, manual segmentation is time consuming, prone to bias, and unsuitable in a clinical setting [10,11]. While several techniques have been proposed to automatically segment some (but not all) ONH tissues in OCT images [10]- [20], each tissue currently requires its own processing algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…For OCT research, manual segmentation has remained the gold-standard to extract structural information of the ONH, and this is especially true for deeper connective tissues [8,9]. However, manual segmentation is time consuming, prone to bias, and unsuitable in a clinical setting [10,11]. While several techniques have been proposed to automatically segment some (but not all) ONH tissues in OCT images [10]- [20], each tissue currently requires its own processing algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, due to numerous patients are not diagnosed in time, they lose their sight needlessly. [8][9][10] It is fact that in comparison to all medicine fields, due to special structure of eye, the ophthalmology is relatively more practical application for AI, ML and DL-assisted automated screening and diagnosis and more open to high technology. Therefore, in the near future, for detecting and treatment of DR, AMD, glaucoma, and other ophthalmic disorders, the unmanned automated applications of AI, ML, and DL will be utilized as a potential alternative to ophthalmologists, retina specialists, and trained human graders.…”
Section: Introductionmentioning
confidence: 99%