Cellpose3 is a novel method designed to improve cellular segmentation in microscopy images, particularly those degraded by noise, blurring, or undersampling. Unlike previous approaches that focus on restoring pixel values, Cellpose3 aims to output images that are well-segmented by a generalist segmentation model while maintaining perceptual similarity to the original images. The method is trained on a large, varied collection of datasets to ensure good generalization to user images. Key features include: 1. **Model Design and Validation**: - **Denoising**: Cellpose3 uses a combination of perceptual and segmentation losses to train a denoising network that outputs images that segment well. - **Deblurring and Upsampling**: The same approach is applied to improve the quality of blurred and downsampled images. - **One-Click Image Restoration**: A single restoration model is trained on all datasets combined, allowing for easy application in the Cellpose GUI without specifying the image type. 2. **Results**: - **Denoising**: Cellpose3 significantly improves segmentation accuracy on noisy images compared to existing methods like Noise2Void and Noise2Self. - **Deblurring and Upsampling**: The method effectively deblurs and upscales images, enhancing segmentation performance. - **Generalization**: The one-click models perform well on various datasets, outperforming dataset-specific models in most cases. 3. **Discussion**: - **Visual Quality**: The restored images are visually compelling and can be used for manual annotations. - **Scientific Research**: The method can be used in scientific research, but it is important to handle denoised images conservatively to avoid potential artifacts. 4. **Methods**: - **Training and Testing**: Detailed descriptions of the training and testing procedures, including the use of different datasets and loss functions. - **Evaluation**: Metrics used to assess the performance of the segmentation algorithms. 5. **References**: - Citations for the datasets and related research. Overall, Cellpose3 provides a robust and user-friendly solution for improving the quality of microscopy images, making it easier for researchers to perform accurate cellular segmentation.Cellpose3 is a novel method designed to improve cellular segmentation in microscopy images, particularly those degraded by noise, blurring, or undersampling. Unlike previous approaches that focus on restoring pixel values, Cellpose3 aims to output images that are well-segmented by a generalist segmentation model while maintaining perceptual similarity to the original images. The method is trained on a large, varied collection of datasets to ensure good generalization to user images. Key features include: 1. **Model Design and Validation**: - **Denoising**: Cellpose3 uses a combination of perceptual and segmentation losses to train a denoising network that outputs images that segment well. - **Deblurring and Upsampling**: The same approach is applied to improve the quality of blurred and downsampled images. - **One-Click Image Restoration**: A single restoration model is trained on all datasets combined, allowing for easy application in the Cellpose GUI without specifying the image type. 2. **Results**: - **Denoising**: Cellpose3 significantly improves segmentation accuracy on noisy images compared to existing methods like Noise2Void and Noise2Self. - **Deblurring and Upsampling**: The method effectively deblurs and upscales images, enhancing segmentation performance. - **Generalization**: The one-click models perform well on various datasets, outperforming dataset-specific models in most cases. 3. **Discussion**: - **Visual Quality**: The restored images are visually compelling and can be used for manual annotations. - **Scientific Research**: The method can be used in scientific research, but it is important to handle denoised images conservatively to avoid potential artifacts. 4. **Methods**: - **Training and Testing**: Detailed descriptions of the training and testing procedures, including the use of different datasets and loss functions. - **Evaluation**: Metrics used to assess the performance of the segmentation algorithms. 5. **References**: - Citations for the datasets and related research. Overall, Cellpose3 provides a robust and user-friendly solution for improving the quality of microscopy images, making it easier for researchers to perform accurate cellular segmentation.