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.
(A) Input image; (B) corresponding ground truth; (C-E) predicted segmentations from (C) ASE_Res_UNet, (D) Pix2pix, and (E) TransUNet models
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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.