# Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/automated-muscle-segmentation-from-clinical-ct-using-bayesian-u-net-for/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yuta Hiasa,Yoshito Otake,Masaki Takao,Takeshi Ogawa,Nobuhiko Sugano,Yoshinobu Sato |
| Citations | 151 |
| DOI | 10.1109/tmi.2019.2940555 |
| Fields | Computer Science,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2972629588 |
| PMID | 31514128 |
| Type | article |
| Year | 2019 |

## Paper authors

- [Masaki Takao](https://scholariq.org/researchers/masaki-takao/)

## Paper primary topic

- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)

## Paper topics

- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.