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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Downloads (Pure)

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 languageEnglish
Title of host publicationTechnical Digest Series (Optica Publishing Group, 2022), paper W2A.5
PublisherOptica Publishing Group
ISBN (Electronic)978-1-957171-12-8
DOIs
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventDigital Holography and 3-D Imaging - Cambridge, United Kingdom
Duration: 1 Aug 20224 Aug 2022

Conference

ConferenceDigital Holography and 3-D Imaging
Country/TerritoryUnited Kingdom
CityCambridge
Period1/08/224/08/22

Publication forum classification

  • Publication forum level 0

Fingerprint

Dive into the research topics of 'Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein'. Together they form a unique fingerprint.

Cite this