ADAPTIVE ATTENTION RESIDUAL U-NET FOR FILAMENT SEGMENTATION IN BIOMEDICAL MICROSCOPY IMAGES
Résumé
Segmenting filamentous structures in biomedical images is challenging, particularly with noisy images and dense filament networks such as those found in vivo. To address this, we created two extensive datasets of synthetic images of fluorescently labeled microtubules, precisely annotated and mimicking real images in terms of noise. The fluorescence intensities varied along the filaments in the second dataset complicating segmentation further. Next, we developed an advanced architecture, the Adaptive Squeeze-and-Excitation Residual U-Net (ASE_Res_Unet), incorporating residual blocks and adaptive SE attention mechanisms and compared it with U-Net. Both visual and quantitative evaluations showed that ASE_Res_Unet outperformed U-Net and two state-of-the art models, especially in segmenting the most challenging dataset. This indicates improved noise handling and the ability to capture finer structural details. Our solution effectively segments noisy or low-contrast biomedical images, demonstrated by our success in segmenting retinal blood vessels, offering promising applications in disease diagnosis and treatment.
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