# Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection

arXiv: https://arxiv.org/abs/2502.09088 · HTML: https://mlsanity.com/paper/2502.09088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors](https://mlsanity.com/paper/2202.11660/) (2022)
- [Implicit field learning for unsupervised anomaly detection in medical images](https://mlsanity.com/paper/2106.05214/) (2021)
- [Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling](https://mlsanity.com/paper/2207.14663/) (2022)
- [MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume](https://mlsanity.com/paper/2305.19920/) (2023)
- [Muscle volume quantification: guiding transformers with anatomical priors](https://mlsanity.com/paper/2310.20355/) (2023)

## Built on by (most cited first, 1 of 1)

- [Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation](https://mlsanity.com/paper/2509.08580/) (2025)

## Abstract (arXiv)

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.