Core Concepts
Cell segmentation models, particularly convolutional neural networks, demonstrate varying robustness to microscope optical aberrations, requiring careful selection based on cell morphology and aberration severity.
Abstract
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.
The key findings are:
For simple cell images, a combination of FPN and SwinS architectures exhibits superior robustness in handling minor aberrations. Cellpose2.0 proves effective for complex cell images under similar conditions.
As aberration amplitude increases, segmentation performance declines sharply, with FPN-SwinS maintaining excellent performance only up to an amplitude of 0.2. Beyond this, optical equipment or post-processing methods are recommended to eliminate aberrations.
For mixed aberrations, SwinS consistently outperforms other backbones, making it suitable for segmentation of simple cell images without the need for additional aberration correction.
Cellpose2.0 shows satisfactory performance on single-class aberrated complex cell images, but its usability is limited to specific cell types (BV2 and SkBr3) under mixed aberrations.
The study provides insights into selecting appropriate segmentation models based on cell morphology and aberration severity, enhancing the reliability of cell segmentation in biomedical applications.
Stats
The amplitude of Zernike polynomials ranges from 0 to 1.0, with sampling intervals of 0.05 and 0.2.
The PSNR values of aberration-degraded cell images decrease sharply as the aberration amplitude increases, stabilizing after an amplitude of 0.4.
Quotes
"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."
"Microscopic artifacts can be partly solved by delicate hardware design or software corrections."