Domain Adaptive Synapse Detection with Weak Point Annotations
This work addresses the challenge of applying synapse detection models across different brain regions with varying data distributions, which is incremental as it builds on existing segmentation methods.
The paper tackles the problem of domain adaptation for synapse detection in electron microscopy images by introducing AdaSyn, a two-stage segmentation-based framework that uses weak point annotations, achieving first place in the WASPSYN challenge at ISBI 2023.
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.