The development of learning-based methods has greatly improved the detection
of synapses from electron microscopy (EM) images. However, training a model for
each dataset is time-consuming and requires extensive annotations.
Additionally, it is difficult to apply a learned model to data from different
brain regions due to variations in data distributions. In this paper, we
present AdaSyn, a two-stage segmentation-based framework for domain adaptive
synapse detection with weak point annotations. In the first stage, we address
the detection problem by utilizing a segmentation-based pipeline to obtain
synaptic instance masks. In the second stage, we improve model generalizability
on target data by regenerating square masks to get high-quality pseudo labels.
Benefiting from our high-accuracy detection results, we introduce the distance
nearest principle to match paired pre-synapses and post-synapses. In the
WASPSYN challenge at ISBI 2023, our method ranks the 1st place.

利用基于分割的 AdaSyn 框架和弱点注释，本研究提出了一种用于领域自适应突触检测的两阶段分割方法，通过高质量伪标签改善模型的普适性，成功应用于不同脑区的数据集，并在 ISBI 2023 的 WASPSYN 挑战中获得第一名。