| Autor: |
Long, Chen, Martin, Strauch, Matthias, Daub, Hans-Georg, Luigs, Marcus, Jansen, Dorit, Merhof |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). |
| DOI: |
10.1109/embc48229.2022.9871517 |
| Popis: |
Image segmentation models trained only with image-level labels have become increasingly popular as they require significantly less annotation effort than models trained with scribble, bounding box or pixel-wise annotations. While methods utilizing image-level labels achieve promising performance for the segmentation of larger-scale objects, they perform less well for the fine structures frequently encountered in biological images. In order to address this performance gap, we propose a deep network architecture based on two key principles, Global Weighted Pooling (GWP) and segmentation refinement by low-level image cues, that, together, make segmentation of fine structures possible. We apply our segmentation method to image datasets containing such fine structures, nematodes (worms + eggs) and nematode cysts immersed in organic debris objects, which is an application scenario encountered in automated soil sample screening. Supervised only with image-level labels, our approach achieves Dice coefficients of 79.72% and 58.51 % for nematode and nematode cyst segmentation, respectively. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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