MTCurv regresses pixel-wise microtubule curvature maps from noisy images using an attention-based residual U-Net trained on synthetic data with a gradient-aware loss.
arXiv preprint arXiv:2011.01118 (2020)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
ASE_Res_UNet with a novel noise-adaptive attention mechanism outperforms ablated variants and alternative architectures in segmenting microtubules from noisy synthetic and real microscopy images while using fewer parameters and transfers to other curvilinear structures.
citing papers explorer
-
MTCurv: Deep learning for direct microtubule curvature mapping in noisy fluorescence microscopy images
MTCurv regresses pixel-wise microtubule curvature maps from noisy images using an attention-based residual U-Net trained on synthetic data with a gradient-aware loss.
-
A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images
ASE_Res_UNet with a novel noise-adaptive attention mechanism outperforms ablated variants and alternative architectures in segmenting microtubules from noisy synthetic and real microscopy images while using fewer parameters and transfers to other curvilinear structures.