{"type":"ScholarlyArticle","title":"Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning","authors":[{"name":"Yinda Chen","slug":"yinda-chen","given":"Yinda","family":"Chen","affiliation":"University of Science and Technology of China; Institute of Artificial Intelligence , Hefei Comprehensive National Science Center"},{"name":"Wei Huang","slug":"wei-huang","given":"Wei","family":"Huang","affiliation":"University of Science and Technology of China"},{"name":"Shenglong Zhou","slug":"shenglong-zhou","given":"Shenglong","family":"Zhou","affiliation":"University of Science and Technology of China"},{"name":"Qi Chen","slug":"qi-chen","given":"Qi","family":"Chen","affiliation":"University of Science and Technology of China"},{"name":"Zhiwei Xiong","slug":"zhiwei-xiong","given":"Zhiwei","family":"Xiong","affiliation":"University of Science and Technology of China; Institute of Artificial Intelligence , Hefei Comprehensive National Science Center"}],"abstract":"The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron microscopy (EM) data. By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, many voxels contain little discriminative information, making MIM pretraining inefficient on the neuron segmentation task. To overcome this challenge, we propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Due to the vast exploration space, using single-agent RL for voxel prediction is impractical. Therefore, we treat each input patch as an agent with a shared behavior policy, allowing for multi-agent collaboration. Furthermore, this multi-agent model can capture dependencies between voxels, which is beneficial for the downstream segmentation task. Experiments conducted on representative EM datasets demonstrate that our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation. Code is available at https://github.com/ydchen0806/dbMiM.","doi":"10.24963/ijcai.2023/68","url":"https://ijcai.org/proceedings/2023/68","pdf":"https://ijcai.org/proceedings/2023/0068.pdf","markdown":"https://ijcai.org/proceedings/2023/68.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/68","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":68,"pages":{"first":609,"last":617},"keywords":["Computer Vision: CV: Biomedical image analysis","Machine Learning: ML: Self-supervised Learning","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Arganda-Carreras et al., 2015] Ignacio Arganda-Carreras, Srinivas C Turaga, Daniel R Berger, Dan Cires ¸an, Alessandro Giusti, Luca M Gambardella, Jürgen Schmid- huber, Dmitry Laptev, Sarvesh Dwivedi, Joachim M Buhmann, et al. 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