2023/08/31 by Qi Chen, Wei Huang, Chen, Qi +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2308.16461
openalex publication_date 2023/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.