Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation

arXiv:2404.00667v1 Announce Type: new
Abstract: Accurate segmentation of organelle instances from electron microscopy (EM) images plays an essential role in many neuroscience researches. However, practical scenarios usually suffer from high annotation costs, label scarcity, and large domain diversity. While unsupervised domain adaptation (UDA) that assumes no annotation effort on the target data is promising to alleviate these challenges, its performance on complicated segmentation tasks is still far from practical usage. To address these issues, we investigate a highly annotation-efficient weak …

abstract annotation arxiv costs cs.cv diversity domain domain adaptation electron however images instances microscopy neuroscience practical role segmentation type unsupervised weakly-supervised