  Cell segmentation is essential in biomedical research for analyzing cellular
morphology and behavior. Deep learning methods, particularly convolutional
neural networks (CNNs), have revolutionized cell segmentation by extracting
intricate features from images. However, the robustness of these methods under
microscope optical aberrations remains a critical challenge. This study
comprehensively evaluates the performance of cell instance segmentation models
under simulated aberration conditions using the DynamicNuclearNet (DNN) and
LIVECell datasets. Aberrations, including Astigmatism, Coma, Spherical, and
Trefoil, were simulated using Zernike polynomial equations. Various
segmentation models, such as Mask R-CNN with different network heads (FPN, C3)
and backbones (ResNet, VGG19, SwinS), were trained and tested under aberrated
conditions. Results indicate that FPN combined with SwinS demonstrates superior
robustness in handling simple cell images affected by minor aberrations.
Conversely, Cellpose2.0 proves effective for complex cell images under similar
conditions. Our findings provide insights into selecting appropriate
segmentation models based on cell morphology and aberration severity, enhancing
the reliability of cell segmentation in biomedical applications. Further
research is warranted to validate these methods with diverse aberration types
and emerging segmentation models. Overall, this research aims to guide
researchers in effectively utilizing cell segmentation models in the presence
of minor optical aberrations.
