  Accurate segmentation of organelle instances, e.g., mitochondria, is
essential for electron microscopy analysis. Despite the outstanding performance
of fully supervised methods, they highly rely on sufficient per-pixel annotated
data and are sensitive to domain shift. Aiming to develop a highly
annotation-efficient approach with competitive performance, we focus on
weakly-supervised domain adaptation (WDA) with a type of extremely sparse and
weak annotation demanding minimal annotation efforts, i.e., sparse point
annotations on only a small subset of object instances. To reduce performance
degradation arising from domain shift, we explore multi-level transferable
knowledge through conducting three complementary tasks, i.e., counting,
detection, and segmentation, constituting a task pyramid with different levels
of domain invariance. The intuition behind this is that after investigating a
related source domain, it is much easier to spot similar objects in the target
domain than to delineate their fine boundaries. Specifically, we enforce
counting estimation as a global constraint to the detection with sparse
supervision, which further guides the segmentation. A cross-position
cut-and-paste augmentation is introduced to further compensate for the
annotation sparsity. Extensive validations show that our model with only 15%
point annotations can achieve comparable performance as supervised models and
shows robustness to annotation selection.
