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

arXiv 2303.01054 ↗First posted 2 Mar 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 2 Mar 2023.

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References · 3

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2017Road Extraction by Deep Residual U-Netcited by 333

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