用隐式神经表示检测肌肉异常，无监督识别肌少症
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection
- 用隐式神经表示建模正常肌肉形状
- 基于重建误差和潜在表征区分肌少症与非肌少症
- 适用于无标注医学影像的肌肉退化分析
肌少症是随年龄增长导致肌肉质量与力量持续下降的疾病，显著影响日常生活。传统评估方法依赖3D影像与人工分割。本文转而研究肌肉形状特征，采用隐式神经表示（INR）建模正常肌肉形态。提出一种无监督异常检测方法，通过隐式模型的重建误差识别肌少症肌肉。结合条件INR与自编码策略，学习肌肉的潜在表征，实现正常与异常肌肉的无监督分离。在103个分割体积数据集上的实验表明，该双重异常检测策略能有效区分肌少症与非肌少症肌肉。
原文摘要 · Abstract (English)
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.
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