{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:37:56Z","timestamp":1753601876964},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Recent advanced proposal-free instance segmentation methods have made significant progress in biological images. However, existing methods are vulnerable to local imaging artifacts and similar object appearances, resulting in over-merge and over-segmentation. To reduce these two kinds of errors, we propose a new biological instance segmentation framework based on a superpixel-guided graph, which consists of two stages, i.e., superpixel-guided graph construction and superpixel agglomeration. Specifically, the first stage generates enough superpixels as graph nodes to avoid over-merge, and extracts node and edge features to construct an initialized graph. The second stage agglomerates superpixels into instances based on the relationship of graph nodes predicted by a graph neural network (GNN). To solve over-segmentation and prevent introducing additional over-merge, we specially design two loss functions to supervise the GNN, i.e., a repulsion-attraction (RA) loss to better distinguish the relationship of nodes in the feature space, and a maximin agglomeration score (MAS) loss to pay more attention to crucial edge classification. Extensive experiments on three representative biological datasets demonstrate the superiority of our method over existing state-of-the-art methods. Code is available at https:\/\/github.com\/liuxy1103\/BISSG.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/169","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"1209-1215","source":"Crossref","is-referenced-by-count":11,"title":["Biological Instance Segmentation with a Superpixel-Guided Graph"],"prefix":"10.24963","author":[{"given":"Xiaoyu","family":"Liu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Yueyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"},{"name":"Institute of Artificial Intelligence, Hefei Comprehensive National Science Center"}]},{"given":"Zhiwei","family":"Xiong","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"},{"name":"Institute of Artificial Intelligence, Hefei Comprehensive National Science Center"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:08:07Z","timestamp":1658142487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/169"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/169","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}