بررسی روش های قطعه بندی لایه های قرنیه در تصاویر توموگرافی انسجام نوری (OCT) و تعیین ضخامت لایه ها

Translated title of the contribution: A Review of OCT Corneal Image Segmentation and Topography of Layer Depths

Samane Ilane, Narges Tabatabaey-Mashadi (Lead / Corresponding author), Ghasem Sadeghi Bajestani, Behzad Barazandeh

Research output: Contribution to journalArticlepeer-review

Abstract

اﻧﺪازهﮔﯿﺮی و ارزﯾﺎﺑﯽ ﺿﺨﺎﻣﺖ ﻻﯾﻪﻫﺎی ﻣﺨﺘﻠـﻒ ﻗﺮﻧﯿـﻪ ﺑـﺮای ﺗﺸـﺨﯿﺺ و درﻣـﺎن ﺑﯿﻤـﺎریﻫـﺎی ﻗﺮﻧﯿـﻪ ﺑﺴـﯿﺎر ﻣﻬـﻢ و ﺿـﺮوری اﺳـﺖ. ﺗﻮﻣﻮﮔﺮاﻓﯽ اﻧﺴﺠﺎم ﻧﻮری)OCT( ﻣﯽﺗﻮاﻧﺪ ﺑﺼـﻮرت ﻏﯿﺮﺗﻬـﺎﺟﻤﯽ و ﻏﯿﺮﺗﻤﺎﺳـﯽ از ﻗﺮﻧﯿـﻪ ﭼﺸـﻢ ﺗﺼـﺎوﯾﺮ ﻣﻘﻄﻌـﯽ در ﻣﻘﯿـﺎس ﻣﯿﮑـﺮون ﺗﻮﻟﯿــﺪ ﮐﻨــﺪ. از آﻧﺠــﺎﯾﯽ ﮐـــﻪ ﻧﺎﺣﯿــﻪﺑﻨــﺪی دﺳــﺘﯽ اﯾـــﻦ ﺗﺼــﺎوﯾﺮ ﺑــﺮای ﺗﻌﯿــﯿﻦ ﻻﯾـــﻪﻫــﺎی ﻗﺮﻧﯿــﻪ، وﻗــﺖﮔﯿـــﺮ اﺳــﺖ، ﻗﻄﻌــﻪﺑﻨـــﺪی ﺧﻮدﮐﺎر و ﺣﺘﯽ ﻧﯿﻤﻪﺧﻮدﮐﺎرِ ﺗﺼﺎوﯾﺮ، ﻣﻄﻠﻮبِ ﭘﺰﺷﮑﺎن اﺳﺖ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺑﺮرﺳﯽ روشﻫـﺎی ﻣﻬـﻢ ﻗﻄﻌـﻪﺑﻨـﺪی ﻻﯾـﻪﻫـﺎی ﻣﺨﺘﻠـﻒ ﻗﺮﻧﯿﻪ در ﺗﺼﺎوﯾﺮ OCT ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. اﯾﻦ روشﻫﺎ در ﺳﻪ ﺑﺨﺶ ﭘﯿﺶﭘﺮدازش، ﻗﻄﻌـﻪﺑﻨـﺪی و ﺗﻮﻟﯿـﺪ ﻧﻘﺸـﻪ ﺿـﺨﺎﻣﺖ، ﻣﻘﺎﯾﺴـﻪ و ﺗﺸﺮﯾﺢ ﺷﺪﻧﺪ. ﻫﺪف ﭘﯿﺶﭘﺮدازشﻫﺎ ﺣﺬف ﻧﻮﯾﺰ و آرﺗﯿﻔﮑﺖ در اﯾﻦ ﻧﻮع ﺗﺼﺎوﯾﺮ ﺑﻮد. ﺑﺮرﺳﯽﻫﺎ ﻧﺸـﺎن داد روشﻫـﺎی ﻣﺒﺘﻨـﯽ ﺑـﺮ ﺗﺒـﺪﯾﻞ ﻫﺎف، ﮐﻪ ﺑﺎ ﺳﺎﺧﺘﺎر ﻗﻮﺳﯽ ﻗﺮﻧﯿﻪ ﻫﻤﺎﻫﻨﮓ اﺳﺖ، در ﻣﻘﺎﯾﺴﻪ ﺑﺎ روشﻫﺎی ﻣﺒﺘﻨﯽ ﺑـﺮ ﮔـﺮاف و آﺳـﺘﺎﻧﻪ، ﻗـﺎدر اﺳـﺖ ﺑـﺎ ﺳـﺮﻋﺖ ﭘـﺮدازش ﻣﻨﺎﺳﺒﯽ ﻣﺮزﻫﺎی دﻗﯿﻖ را اﺳﺘﺨﺮاج ﮐﻨﺪ. ﺑـﺎ اﯾـﻦ وﺟـﻮد، روﯾﮑـﺮد ﺟﺪﯾـﺪ ﻫـﻮش ﻣﺼـﻨﻮﻋﯽ و ﯾـﺎدﮔﯿﺮی ﻋﻤﯿـﻖ در ﻗﻄﻌﻪ ﺑﻨـﺪی، اﻓﻖ ﻫـﺎی ﺗﺎزه ای را در ﺗﺤﻠﯿﻞ اﯾﻦ ﻧﻮع ﺗﺼﺎوﯾﺮ ﺑﺎز ﮐﺮده اﺳﺖ. ﻫﺪف ﭘﮋوﻫﺶﻫﺎ اراﺋﻪ ﺑﻬﯿﻨﻪ اﻃﻼﻋـﺎت ﺗﺼـﺎوﯾﺮ ﺑـﺮای ﮐﻤـﮏ ﺑـﻪ ﭼﺸﻢ ﭘﺰﺷـﮑﺎن در ﺗﺸﺨﯿﺺ ﺑﻬﺘﺮ و درﻣﺎن آﺳﯿﺐﻫﺎی ﻗﺮﻧﯿﻪ اﺳﺖ؛ ﺑﻨﺎﺑﺮاﯾﻦ ﻣﯽﺗﻮان ﮔﻔـﺖ ﺗﻮﻟﯿـﺪ ﻧﻘﺸـﻪ ﺿـﺨﺎﻣﺖ ﻻﯾـﻪﻫـﺎ، ﮐـﻪ ﻧﯿﺎزﻣﻨـﺪ ﭘـﺮدازشِ ﺧﻮدﮐـﺎرِ ﻣﺠﻤﻮﻋﻪای از ﺗﺼﺎوﯾﺮ ِﺳﻄﺢ ﻣﻘﻄﻊ اﺳﺖ، ﺧﺮوﺟﯽ ﻣﻬﻤﯽ اﺳﺖ ﮐﻪ در ﭘﮋوﻫﺶﻫﺎی ﮐﻤﺘﺮی ﺑﻪ آن ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ.

Abstract in English

Thickness evaluation and analysis of corneal layers are important for diagnosis and treatments considering corneal disease. Optical Coherence Tomography (OCT) can produce micron-scaled cross-sectional images in a non-invasive and non-contacting manner. Since manual segmentation and layer detection within such images are time-consuming, physicians prefer automatic/semi-automatic methods. This paper reviewed main and important methods of corneal layer segmentationsapplied to OCT images. The methods are compared and described in three categories: preprocessing, segmentation, and thickness mapping (layers’ topography). The purpose of preprocessing was to remove noise and artifacts from such OCT images. Studies show that methods based on Hough transform, which are consistent with the corneal arc structure when compared to graph and threshold methods, are able to extract accurate boundaries in a reasonable time. Meanwhile, artificial intelligence and the deep learning approach has opened new horizons in segmentation and analysis of such images. In studies, generally the aim was to extract and present OCT image information in a form that would help ophthalmologists better diagnose and treat corneal abnormalities; therefore it can be concluded thatlayer topography and its related issues that require automatic processing of a set of cross-sectional images, isan important output that is not addressed in many research.
Translated title of the contributionA Review of OCT Corneal Image Segmentation and Topography of Layer Depths
Original languagePersian (Iran, Islamic Republic of)
Pages (from-to)119-136
Number of pages18
JournalMachine Vision and Image Processing
Volume7
Issue number2
Publication statusPublished - Feb 2021

Keywords

  • Optical Coherence Tomography (OCT)
  • OCT Corneal Image Segmentation
  • Corneal layer detection
  • Corneal layer boundary detection
  • Corneal image Processing
  • Thickness map of the cornea layers (cornea layer topography)

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