Abstract
Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biological and neuroscience research. Although unsupervised domain adaptation (UDA) methods can help mitigate domain shifts and reduce the high costs of annotating each domain, they typically have relatively low performance in practical applications. Thus, we investigate weakly supervised domain adaptation (WDA) that utilizes additional sparse point labels on the target domain, which require minimal annotation effort and minimal expert knowledge. To take full use of the incomplete and imprecise point annotations, we introduce a multitask learning framework that jointly conducts segmentation and center detection with a novel cross-teaching mechanism and class-focused cross-domain contrastive learning. While leveraging unlabeled image regions is essential, we introduce segmentation self-training with a novel instance-aware pseudo-label (IPL) selection strategy. Unlike existing methods that typically rely on pixel-wise pseudo-label filtering, the IPL semantically selects reliable and diverse pseudo-labels with the help of the detection task. Comprehensive validations and comparisons on challenging datasets demonstrate that our method outperforms existing UDA and WDA methods, significantly narrowing the performance gap with the supervised upper bound. Furthermore, under the UDA setting, our method also achieves substantial improvements over other UDA techniques.
Abstract (translated)
从各种电子显微镜（EM）图像中高效注释众多线粒体实例对于生物和神经科学研究非常有价值。虽然无监督领域适应（UDA）方法可以帮助缓解域偏移并减少对每个域进行详细标注的高昂成本，但它们在实际应用中的表现通常相对较低。因此，我们研究了弱监督领域适应（WDA），该方法利用目标域上的稀疏点标签，这些标签只需要最小的努力和专业知识即可完成注释。为了充分利用不完整且不够精确的点注释，我们引入了一个多任务学习框架，该框架同时进行分割和中心检测，并结合了一种新颖的跨教学机制以及以类别为中心的跨领域对比学习方法。 在利用未标注图像区域方面至关重要，我们引入了基于实例感知伪标签（IPL）选择策略的分割自训练。不同于现有方法通常依赖像素级伪标签过滤，IPL通过检测任务的帮助进行语义上可靠和多样化的伪标签选取。在具有挑战性的数据集上的全面验证和比较表明，我们的方法优于现有的UDA和WDA方法，并显著缩小了与监督上限性能差距。此外，在UDA设置下，我们的方法也比其他UDA技术实现了重大改进。
URL
https://arxiv.org/abs/2510.16450
