{"type":"ScholarlyArticle","title":"SPGNet: A Shape-prior Guided Network for Medical Image Segmentation","authors":[{"name":"Zhengxuan Song","slug":"zhengxuan-song","given":"Zhengxuan","family":"Song","affiliation":"sen University"},{"name":"Xun Liu","slug":"xun-liu","given":"Xun","family":"Liu","affiliation":"The Third Affiliated Hospital of Sun Yat-sen University"},{"name":"Wenhao Zhang","slug":"wenhao-zhang","given":"Wenhao","family":"Zhang","affiliation":"University of the West of England"},{"name":"Yongyi Gong","slug":"yongyi-gong","given":"Yongyi","family":"Gong","affiliation":"Guangdong University of Foreign Studies"},{"name":"Tianyong Hao","slug":"tianyong-hao","given":"Tianyong","family":"Hao","affiliation":"South China Normal University"},{"name":"Kun Zeng","slug":"kun-zeng","given":"Kun","family":"Zeng","affiliation":"sen University"}],"abstract":"Given the intricacy and variability of anatomical structures in medical images, some methods employ shape priors to constrain segmentation. However, limited by the representational capability of these priors, existing approaches often struggle to capture diverse target structure morphologies. To address this, we propose SPGNet to guide segmentation by fully exploiting category-specific shape knowledge. The key idea is to enable the network to perceive data shape distributions by learning from statistical shape models. We uncover shape relationships via clustering and obtain statistical prior knowledge using principal component analysis. Our dual-path network comprises a segmentation path and a shape-prior path that collaboratively discern and harness shape prior distribution to improve segmentation robustness. The shape-prior path further serves to refine shapes iteratively by cropping features from the segmentation path, guiding the segmentation path and directing attention specifically to the edges of shapes which could be most significantly susceptible to segmentation error. We demonstrate superior performance on chest X-ray and breast ultrasound benchmarks.","doi":"10.24963/ijcai.2024/140","url":"https://ijcai.org/proceedings/2024/140","pdf":"https://ijcai.org/proceedings/2024/0140.pdf","markdown":"https://ijcai.org/proceedings/2024/140.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/140","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":140,"pages":{"first":1263,"last":1271},"keywords":["Computer Vision: CV: Biomedical image analysis","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Al-Dhabyani et al., 2020] Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy. 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