Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912558949072896 |
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| author | Docherty, Ronan Vamvakeros, Antonis Cooper, Samuel J. |
| author_facet | Docherty, Ronan Vamvakeros, Antonis Cooper, Samuel J. |
| contents | Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these descriptors are often patch-based, and so struggle to represent the fine features often present in micrographs; they also struggle with the large image sizes present in materials and biological image analysis. In this work, we train a convolutional neural network to upsample low-resolution (i.e, large patch size) foundation model features with reference to the input image. We apply this upsampler network (without any further training) to efficiently featurise and then segment a variety of microscopy images, including plant cells, a lithium-ion battery cathode and organic crystals. The richness of these upsampled features admits separation of hard to segment phases, like hairline cracks. We demonstrate that interactive segmentation with these deep features produces high-quality segmentations far faster and with far fewer labels than training or finetuning a more traditional convolutional network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21529 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Docherty, Ronan Vamvakeros, Antonis Cooper, Samuel J. Computer Vision and Pattern Recognition Materials Science Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these descriptors are often patch-based, and so struggle to represent the fine features often present in micrographs; they also struggle with the large image sizes present in materials and biological image analysis. In this work, we train a convolutional neural network to upsample low-resolution (i.e, large patch size) foundation model features with reference to the input image. We apply this upsampler network (without any further training) to efficiently featurise and then segment a variety of microscopy images, including plant cells, a lithium-ion battery cathode and organic crystals. The richness of these upsampled features admits separation of hard to segment phases, like hairline cracks. We demonstrate that interactive segmentation with these deep features produces high-quality segmentations far faster and with far fewer labels than training or finetuning a more traditional convolutional network. |
| title | Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation |
| topic | Computer Vision and Pattern Recognition Materials Science |
| url | https://arxiv.org/abs/2508.21529 |