Schistosomiasis is a parasitic disease with significant global health and socio-economic implications. Drug discovery for schistosomiasis typically involves high-content whole-organism screening. In this approach, parasites are exposed to various chemical compounds and their systemic, whole-organism-level responses are captured via microscopy and analyzed to obtain a quantitative assessment of chemical effect. These effects are multidimensional and time-varying, impacting shape, appearance, and behavior. Accurate identification of object boundaries is essential for preparing images for subsequent analysis in high-content studies. Object segmentation is one of the most deeply studied problems in computer vision where recent efforts have incorporated deep learning. Emerging results indicate that acquiring robust features in spectral domain using Fast Fourier Transform (FFT) within Deep Neural Networks (DNNs) can enhance segmentation accuracy. In this paper, we explore this direction further and propose a latent space Phase-Gating (PG) method that builds upon FFT and leverages phase information to efficiently identify globally significant features. While the importance of phase in analyzing signals has long been known, technical difficulties in calculating phase in manners that are invariant to imaging parameters has limited its use. A key result of this paper is to show how phase information can be incorporated in neural architectures that are compact. Experiments conducted on complex HCS datasets demonstrate how this idea leads to improved segmentation accuracy, while maintaining robustness against commonly encountered noise (blurring) in HCS. The compactness of the proposed method also makes it well-suited for application specific architectures (ASIC) designed for high-content screening.