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Open AccessArticle
Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins
by
Maryam Viqar
Maryam Viqar 1,2,*,
Violeta Madjarova
Violeta Madjarova 1,
Elena Stoykova
Elena Stoykova 1,*,
Dimitar Nikolov
Dimitar Nikolov 3,
Ekram Khan
Ekram Khan 4 and
Keehoon Hong
Keehoon Hong 5
1
Institute of Optical Materials and Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
2
Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
3
Department of Vascular Surgery, Sofiamed University Hospital, 1797 Sofia, Bulgaria
4
Department of Electronics Engineering, Aligarh Muslim University, Aligarh 202001, India
5
Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
*
Authors to whom correspondence should be addressed.
Micromachines 2024, 15(7), 902; https://doi.org/10.3390/mi15070902 (registering DOI)
Submission received: 30 May 2024
/
Revised: 6 July 2024
/
Accepted: 9 July 2024
/
Published: 10 July 2024
Abstract
In-depth mechanical characterization of veins is required for promising innovations of venous substitutes and for better understanding of venous diseases. Two important physical parameters of veins are shape and thickness, which are quite challenging in soft tissues. Here, we propose the method TREE (TransfeR learning-based approach for thicknEss Estimation) to predict both the segmentation map and thickness value of the veins. This model incorporates one encoder and two decoders which are trained in a special manner to facilitate transfer learning. First, an encoder–decoder pair is trained to predict segmentation maps, then this pre-trained encoder with frozen weights is paired with a second decoder that is specifically trained to predict thickness maps. This leverages the global information gained from the segmentation model to facilitate the precise learning of the thickness model. Additionally, to improve the performance we introduce a sensitive pattern detector (SPD) module which further guides the network by extracting semantic details. The swept-source optical coherence tomography (SS-OCT) is the imaging modality for saphenous varicose vein extracted from the diseased patients. To demonstrate the performance of the model, we calculated the segmentation accuracy—0.993, mean square error in thickness (pixels) estimation—2.409 and both these metrics stand out when compared with the state-of-art methods.
Share and Cite
MDPI and ACS Style
Viqar, M.; Madjarova, V.; Stoykova, E.; Nikolov, D.; Khan, E.; Hong, K.
Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins. Micromachines 2024, 15, 902.
https://doi.org/10.3390/mi15070902
AMA Style
Viqar M, Madjarova V, Stoykova E, Nikolov D, Khan E, Hong K.
Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins. Micromachines. 2024; 15(7):902.
https://doi.org/10.3390/mi15070902
Chicago/Turabian Style
Viqar, Maryam, Violeta Madjarova, Elena Stoykova, Dimitar Nikolov, Ekram Khan, and Keehoon Hong.
2024. "Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins" Micromachines 15, no. 7: 902.
https://doi.org/10.3390/mi15070902
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