Domain Adaptive Segmentation of Electron Microscopy with Sparse Point Annotations

• URL: http://arxiv.org/abs/2210.13109v3
• Date: Thu, 27 Oct 2022 10:56:18 GMT
• Title: Domain Adaptive Segmentation of Electron Microscopy with Sparse Point Annotations
• Authors: Dafei Qiu, Jiajin Yi, Jialin Peng
• Abstract summary: We 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. We show that our model with only 15% point annotations can achieve comparable performance as supervised models.
• Score: 2.5137859989323537
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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