Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Maryam Viqar; Violeta Madjarova; Amit Kumar Yadav; Desislava Pashkuleva; Alexander S. Machikhin

doi:10.1364/DH.2022.W2A.5

Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Maryam Viqar, Violeta Madjarova, Amit Kumar Yadav, Desislava Pashkuleva, Alexander S. Machikhin

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

Deep-learning based segmentation model is proposed for Optical Coherence
Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

Original language	English
Title of host publication	Technical Digest Series (Optica Publishing Group, 2022), paper W2A.5
ISBN (Electronic)	978-1-957171-12-8
DOIs	https://doi.org/10.1364/DH.2022.W2A.5
Publication status	Published - 2022
Publication type	A4 Article in conference proceedings
Event	Digital Holography and 3-D Imaging - Cambridge, United Kingdom Duration: 1 Aug 2022 → 4 Aug 2022

Conference

Conference	Digital Holography and 3-D Imaging
Country/Territory	United Kingdom
City	Cambridge
Period	1/08/22 → 4/08/22

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DH PAPERAccepted author manuscript, 253 KBLicence: CC BY-NC-ND

https://urn.fi/URN:NBN:fi:tuni-202303082815Licence: Unspecified

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@inproceedings{6e55a318f8134a2ca3d778695051313c,

title = "Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein",

abstract = " Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.",

author = "Maryam Viqar and Violeta Madjarova and Yadav, {Amit Kumar} and Desislava Pashkuleva and Machikhin, {Alexander S.}",

year = "2022",

doi = "10.1364/DH.2022.W2A.5",

language = "English",

booktitle = "Technical Digest Series (Optica Publishing Group, 2022), paper W2A.5",

note = "Digital Holography and 3-D Imaging ; Conference date: 01-08-2022 Through 04-08-2022",

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T1 - Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

AU - Viqar, Maryam

AU - Madjarova, Violeta

AU - Yadav, Amit Kumar

AU - Pashkuleva, Desislava

AU - Machikhin, Alexander S.

PY - 2022

Y1 - 2022

N2 - Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

AB - Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

U2 - 10.1364/DH.2022.W2A.5

DO - 10.1364/DH.2022.W2A.5

M3 - Conference contribution

BT - Technical Digest Series (Optica Publishing Group, 2022), paper W2A.5

T2 - Digital Holography and 3-D Imaging

Y2 - 1 August 2022 through 4 August 2022

ER -

Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

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