Semantic segmentation of mitochondria is essential for electron microscopy image analysis. Despite the great success achieved using supervised learning, it requires a large amount of expensive per-pixel annotations. Recent studies have proposed to exploit similar but annotated domains by domain adaptation, but the possible severe domain shift poses a challenge for the model transfer. In this study, we develop an unsupervised domain adaptation method to adapt the model trained on an labeled source domain to the unlabeled target domain. Specifically, we achieve cross-domain segmentation by integrating geometrical cues provided by the annotated labels and the visual cues latent in images of both domains in a framework of adversarial domain adaptive multi-task learning. Rather than enforcing manually-defined shape priors, we propose to learn geometrical cues from the source domain through adversarial learning. Domain-invariant and discriminative features are learned through joint adaptation. Extensive ablations, parameter analysis and comparisons have been conducted on three benchmarks under various settings. The experiments show that our method performs favorably against state-of-the-art methods both in segmentation accuracy and visual quality.

中文翻译：

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通过域自适应多任务学习在 EM 图像中进行无监督线粒体分割

线粒体的语义分割对于电子显微镜图像分析至关重要。尽管使用监督学习取得了巨大成功，但它需要大量昂贵的逐像素注释。最近的研究提出通过域适应来利用相似但带注释的域，但可能严重的域转移对模型转移提出了挑战。在这项研究中，我们开发了一种无监督域适应方法，使在标记源域上训练的模型适应未标记的目标域。具体来说，我们通过在对抗域自适应多任务学习的框架中整合注释标签提供的几何线索和两个域图像中潜在的视觉线索来实现跨域分割。而不是强制执行手动定义的形状先验，我们建议通过对抗性学习从源域中学习几何线索。通过联合适应学习域不变和判别特征。在各种设置下对三个基准进行了广泛的消融、参数分析和比较。实验表明，我们的方法在分割精度和视觉质量方面均优于最先进的方法。