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Journal Article
Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins
- Authors
- Maryam Viqar, Violeta Madjarova, Elena Stoykova, Dimitar Nikolov, Ekram Khan, Keehoon Hong
- Issue Date
- 2024-07
- Citation
- Micromachines, v.15, no.7, pp.1-15
- ISSN
- 2072-666X
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Based Approach, Learning-based, Mechanical characterization, Optical Coherence Tomography(OCT), Physical parameters, SS-OCT, Segmentation Accuracy, Segmentation map, Soft tissue, State-of-art, Transfer learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
