Related Experiment Video
Updated: Jun 11, 2025
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Published on: August 13, 2014
Practical guidelines for cell segmentation models under optical aberrations in microscopy
Boyuan Peng1,2,3, Jiaju Chen1,2, P Bilha Githinji1,2
1Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
This study evaluates deep learning cell segmentation models under microscope optical aberrations. Cellpose 2.0 and FPN with SwinS backbones show robustness, with a new model (PLCM) aiding aberration identification for better cell analysis.
Area of Science:
- Biomedical imaging
- Computational biology
- Microscopy
Background:
- Cell segmentation is crucial for analyzing cellular morphology and behavior in biomedical research.
- Deep learning, especially Convolutional Neural Networks (CNNs), excels at cell segmentation but struggles with optical aberrations.
- Evaluating model robustness under various aberrations is vital for reliable biological image analysis.
Purpose of the Study:
- To assess the performance of cell image segmentation models under simulated optical aberrations.
- To compare the robustness of different deep learning models and traditional methods on fluorescence and bright field microscopy datasets.
- To introduce a novel model for identifying aberration types and amplitudes to guide segmentation model selection.
Main Methods:
- Simulated various optical aberrations (astigmatism, coma, spherical aberration, trefoil, mixed).
- Evaluated Otsu threshold, Mask R-CNN (with FPN/C3 heads, ResNet/VGG/Swin Transformer backbones), and Cellpose 2.0 on DynamicNuclearNet and LIVECell datasets.
- Developed and validated the Point Spread Function Image Label Classification Model (PLCM) for aberration characterization.
Main Results:
- The FPN with SwinS backbone combination showed superior robustness for simple cell images with minor aberrations.
- Cellpose 2.0 demonstrated effectiveness for complex cell images under similar aberrated conditions.
- PLCM accurately identified aberration types and amplitudes from Point Spread Function (PSF) images.
Conclusions:
- Specific deep learning architectures offer varying degrees of robustness against optical aberrations in cell segmentation.
- Cellpose 2.0 is recommended for complex cell images with aberrations.
- PLCM aids researchers in selecting appropriate segmentation strategies by characterizing optical aberrations, improving the reliability of cell analysis.
