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

arXiv: https://arxiv.org/abs/1907.08915 · HTML: https://mlsanity.com/paper/1907.08915/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

## Built on by (most cited first, 22 of 22)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
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- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.16298/) (2024)
- [Bayesian U-Net for Segmenting Glaciers in SAR Imagery](https://mlsanity.com/paper/2101.03249/) (2021)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps](https://mlsanity.com/paper/2102.11865/) (2021)
- [Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities](https://mlsanity.com/paper/2307.13986/) (2023)
- [BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning](https://mlsanity.com/paper/2207.03210/) (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)
- [Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks](https://mlsanity.com/paper/2306.16556/) (2023)
- [Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography](https://mlsanity.com/paper/2307.11513/) (2023)
- [Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs](https://mlsanity.com/paper/2401.00159/) (2023)
- [3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation](https://mlsanity.com/paper/2409.16702/) (2024)
- [Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers](https://mlsanity.com/paper/2101.11476/) (2021)
- [Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation](https://mlsanity.com/paper/2211.00303/) (2022)
- [Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images](https://mlsanity.com/paper/2409.02770/) (2024)
- [Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection](https://mlsanity.com/paper/2301.10441/) (2023)
- [Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image](https://mlsanity.com/paper/2407.20495/) (2024)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning](https://mlsanity.com/paper/2509.10784/) (2025)

## Abstract (arXiv)

We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.