Marine microorganisms are fundamental to marine ecosystems. However, underwater imaging often blurs microbial contours due to water absorption and scattering. To address this, we propose a contour segmentation method for underwater microorganisms that combines an underwater imaging model with Fourier descriptors. First, the background light and water attenuation coefficients are estimated using the underwater imaging model to extract a clear, water-free feature map of the object. Next, a classification header determines the target location, while a regression header uses Fourier descriptors to represent and refine the microorganism's contour in the pixel domain. In addition, hologram reconstruction and preprocessing steps are applied, and a microbial contour segmentation dataset is generated. Experimental results demonstrate that the Fourier descriptor outperforms the star polygon method in contour representation accuracy and spatial continuity. Compared to traditional segmentation methods, the proposed algorithm achieves an F₁ score of 0.8894, intersection over union of 0.7887, and pixel accuracy of 0.8608, all improved metrics indicating superior segmentation capability.