Evaluating Deep Learning Architectures for Actin Filament Segmentation under Varying Noise Conditions in Simulated Cryo-ET Tomograms.

Actin filaments are fundamental components of the cytoskeleton, essential for maintaining cell shape, enabling motility, and facilitating intracellular transport. Cryo-electron tomography (cryo-ET) enables nanometer-resolution visualization of filament networks in situ; however, low signal-to-noise ratios, missing wedge artifacts, and complex 3D architectures present significant challenges for accurate analysis. Manual filament annotation is highly resource-intensive and prone to variability, underscoring the need for automated approaches. In this study, we develop and evaluate deep learning-based semantic segmentation architectures for accurate segmentation of actin filament networks in cryo-ET data. We systematically assess several architectures-3D U-Net, Attention 3D U-Net, TransUNet 3D, and UNETR-on simulated tomograms with known ground truth. Model performance is quantified using Dice scores and Intersection over Union (IoU) to evaluate segmentation performance under challenging imaging conditions. Our results show that no single deep learning architecture consistently outperforms others, highlighting the importance of accounting for filament arrangement and noise characteristics. By providing a comparative evaluation of these architectures, we demonstrate their effectiveness in detecting filamentous structures and offer guidance for future efforts to improve segmentation performance.