  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
