Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916612085383168 |
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| author | Piecuch, Louise Huet, Jeremie Frouin, Antoine Nordez, Antoine Boureau, Anne-Sophie Mateus, Diana |
| author_facet | Piecuch, Louise Huet, Jeremie Frouin, Antoine Nordez, Antoine Boureau, Anne-Sophie Mateus, Diana |
| contents | Sarcopenia is an age-related progressive loss of muscle mass and strength that significantly impacts daily life. A commonly studied criterion for characterizing the muscle mass has been the combination of 3D imaging and manual segmentations. In this paper, we instead study the muscles' shape. We rely on an implicit neural representation (INR) to model normal muscle shapes. We then introduce an unsupervised anomaly detection method to identify sarcopenic muscles based on the reconstruction error of the implicit model. Relying on a conditional INR with an auto-decoding strategy, we also learn a latent representation of the muscles that clearly separates normal from abnormal muscles in an unsupervised fashion. Experimental results on a dataset of 103 segmented volumes indicate that our double anomaly detection strategy effectively discriminates sarcopenic and non-sarcopenic muscles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection Piecuch, Louise Huet, Jeremie Frouin, Antoine Nordez, Antoine Boureau, Anne-Sophie Mateus, Diana Computer Vision and Pattern Recognition Machine Learning Sarcopenia is an age-related progressive loss of muscle mass and strength that significantly impacts daily life. A commonly studied criterion for characterizing the muscle mass has been the combination of 3D imaging and manual segmentations. In this paper, we instead study the muscles' shape. We rely on an implicit neural representation (INR) to model normal muscle shapes. We then introduce an unsupervised anomaly detection method to identify sarcopenic muscles based on the reconstruction error of the implicit model. Relying on a conditional INR with an auto-decoding strategy, we also learn a latent representation of the muscles that clearly separates normal from abnormal muscles in an unsupervised fashion. Experimental results on a dataset of 103 segmented volumes indicate that our double anomaly detection strategy effectively discriminates sarcopenic and non-sarcopenic muscles. |
| title | Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.09088 |