  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.

Computer Vision and Pattern Recognition