The combination of Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs) has revolutionized the understanding of complicated biological structures and greatly advanced bio imaging. By automatically learning hierarchical characteristics from huge datasets, CNN especially in U-Net architectures have shown to be incredibly adept at segmenting a wide range of biological entities, including cells, tissues, and neurons. By substituting convolutional layers with fully connected layers, FCNs expand the capabilities of CNNs and allow end-to-end training for dense prediction problems. With the help of this technique, segmentation may be done precisely in space, collecting minute details and spatial relationships that are essential for biological imaging. FCNs perform better than typical CNNs in comparison, as evidenced by comparative evaluations of Dice Coefficient, Intersection over Union (IoU), precision, and recall. As a result, FCNs are especially well-suited for segmenting structures such as blood arteries and neurons. In addition to improving segmentation robustness and accuracy, these developments have the potential to revolutionize the biological sciences by facilitating more accurate diagnosis, individualized therapy, and a better comprehension of intricate biological systems.