Human veins are important for carrying the blood from the body-parts to the heart. The improper functioning of the human veins may arise from several venous diseases. Varicose vein is one such disease wherein back flow of blood can occur, often resulting in increased venous pressure or restricted blood flow due to changes in the structure of vein. To examine the functional characteristics of the varicose vein, it is crucial to study the physical and bio mechanical properties of the vein. This work proposes a segmentation model Opto-UNet, for segmenting the venous wall structure. Optical Coherence Tomography system is used to acquire images of varicose vein. As the extracted vein is not uniform in shape, hence adequate method of segmentation is required to segment the venous wall. Opto-UNet model is based on the U-Net architecture wherein a new block is integrated into the architecture, employing atrous and separable convolution to extract spatially wide-range and separable features maps for attaining advanced performance. Furthermore, the depth wise separable convolution significantly reduces the complexity of the network by optimizing the number of parameters. The model achieves accuracy of 0.9830, sensitivity of 0.8425 and specificity of 0.9980 using 8.54 million number of parameters. These results indicate that model is highly adequate in segmenting the varicose vein wall without deteriorating the segmentation quality along with reduced complexity

翻译：人体静脉在将血液从身体各部位输送至心脏的过程中发挥重要作用。静脉功能异常可能由多种静脉疾病引起，其中静脉曲张是一种常见疾病，常因静脉结构改变导致血液回流、静脉压升高或血流受阻。为研究静脉曲张的功能特性，探究其物理与生物力学特征至关重要。本文提出一种名为Opto-UNet的分割模型，用于分割静脉壁结构。研究采用光学相干断层成像系统获取静脉曲张图像。由于提取的静脉形状不规则，需采用合适的分割方法对静脉壁进行分割。Opto-UNet模型基于U-Net架构，通过集成新型模块，利用空洞卷积和可分离卷积提取空间宽范围及可分离特征图，从而提升性能。此外，深度可分离卷积通过优化参数数量显著降低了网络复杂度。该模型在854万个参数下实现了0.9830的准确率、0.8425的敏感度和0.9980的特异度。结果表明，该模型在降低复杂度的同时，能够高精度分割静脉曲张血管壁且不损害分割质量。