Domain Adaptive Synapse Detection with Weak Point Annotations

• URL: http://arxiv.org/abs/2308.16461v1
• Date: Thu, 31 Aug 2023 05:05:53 GMT
• Title: Domain Adaptive Synapse Detection with Weak Point Annotations
• Authors: Qi Chen, Wei Huang, Yueyi Zhang, Zhiwei Xiong
• Abstract summary: We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
• Score: 63.97144211520869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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