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.

翻译：基于学习的方法的发展极大地提高了从电子显微镜（EM）图像中检测突触的能力。然而，为每个数据集训练模型既耗时又需要大量标注。此外，由于不同大脑区域的数据分布差异，将训练好的模型应用于其他区域的数据十分困难。本文提出了AdaSyn——一种基于弱标注点注释的两阶段分割框架，用于域自适应突触检测。在第一阶段，我们利用基于分割的流水线获取突触实例掩码，从而解决检测问题。在第二阶段，我们通过重新生成方形掩码来获取高质量伪标签，从而提高模型在目标数据上的泛化能力。得益于我们的高精度检测结果，我们引入距离最近原则来匹配成对的前突触和后突触。在ISBI 2023的WASPSYN挑战赛中，我们的方法获得了第一名。