MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

Planned MuscleMap Phases

Develop a standardized acquisition protocol for whole-body quantitative MRI of muscle for the most common MR manufacturers.
Generate an open-source large (n≥1,000) annotated multi-site, multi-racial, and multi-ethnic heterogenous whole-body muscle MRI dataset across the lifespan using the standardized acquisition protocol.
Create an open-source toolbox for the analysis of whole-body muscle morphometry and composition using the heterogenous whole-body muscle MRI dataset.

Standardized Acquisition Protocol

We are currently developing the standardized acquisition protocol for whole-body quantitative MRI of muscle. You can access the Google doc here. To collaborate on the standardized acquisition protocol, please contact us.

Data Curation

We strongly recommend following the Brain Imaging Data Structure (BIDS) specification for organizing your dataset.

Convert DICOM to BIDS

Convert images from DICOM format to NIfTI format.
- We recommend using dcm2niix and working with compressed NIfTI files (nii.gz).
- Keep the json sidecar file, which contains imaging parameters.

Rename the NIfTI and json files and organize your dataset to follow the BIDS specification.

Click to see an example BIDS directory structure.

```
dataset
├── derivatives
│   └── labels
│      └── sub-example01
│      └── sub-example02
│           ├── ses-abdomen
│           │   └── anat
│           │       ├── sub-example02_ses-abdomen_T2w_label-muscle_dseg.json
│           │       └── sub-example02_ses-adomen_T2w_label-muscle_dseg.nii.gz
│           │       ├── sub-example02_ses-abdomen_water_label-muscle_dseg.json
│           │       └── sub-example02_ses-adomen_water_label-muscle_dseg.nii.gz
│           └── ses-neck
│               └── anat
│                   ├── sub-example02_ses-neck_water_label-muscle_dseg.json
│                   └── sub-example02_ses-neck_water_label-muscle_dseg.nii.gz
└── sourcedata
    └── participants.tsv
    └── sub-example01
    └── sub-example02
        ├── ses-abdomen
        │   ├── anat
        │   │   ├── sub-example02_ses-abdomen_fatfrac.json
        │   │   ├── sub-example02_ses-abdomen_fatfrac.nii.gz
        │   │   ├── sub-example02_ses-abdomen_fat.json
        │   │   ├── sub-example02_ses-abdomen_fat.nii.gz
        │   │   ├── sub-example02_ses-abdomen_inphase.json
        │   │   ├── sub-example02_ses-abdomen_inphase.nii.gz
        │   │   ├── sub-example02_ses-abdomen_outphase.json
        │   │   ├── sub-example02_ses-abdomen_outphase.nii.gz
        │   │   ├── sub-example02_ses-abdomen_R2star.json
        │   │   ├── sub-example02_ses-abdomen_R2star.nii.gz
        │   │   ├── sub-example02_ses-abdomen_T1w.json
        │   │   ├── sub-example02_ses-abdomen_T1w.nii.gz
        │   │   ├── sub-example02_ses-abdomen_T2w.json
        │   │   ├── sub-example02_ses-abdomen_T2w.nii.gz
        │   │   ├── sub-example02_ses-abdomen_water.json
        │   │   └── sub-example02_ses-abdomen_water.nii.gz
        │   └── dwi
        │       ├── sub-example02_ses-abdomen_dwi.bval
        │       ├── sub-example02_ses-abdomen_dwi.bvec
        │       ├── sub-example02_ses-abdomen_dwi.json
        │       └── sub-example02_ses-abdomen_dwi.nii.gz
        └── ses-neck
            └── anat
                ├── sub-example02_ses-neck_fat.json
                ├── sub-example02_ses-neck_fat.nii.gz
                ├── sub-example02_ses-neck_fatfrac.json
                ├── sub-example02_ses-neck_fatfrac.nii.gz
                ├── sub-example02_ses-neck_inphase.json
                ├── sub-example02_ses-neck_inphase.nii.gz
                ├── sub-example02_ses-neck_outphase.json
                ├── sub-example02_ses-neck_outphase.nii.gz
                ├── sub-example02_ses-neck_R2star.json
                ├── sub-example02_ses-neck_R2star.nii.gz
                ├── sub-example02_ses-neck_T2w.json
                ├── sub-example02_ses-neck_T2w.nii.gz
                ├── sub-example02_ses-neck_water.json
                └── sub-example02_ses-neck_water.nii.gz
```

sourcedata = contains participants.tsv, raw images, json sidecar files, and no other files
derivatives = contains segmentation images and any other derivatives
If you have a large dataset to convert, the DICOM to BIDS conversion can be automated. If needed, feel free to reach out to us for help automating the conversion.

MuscleMap Toolbox

We provide a step-by-step installation and usage tutorial video here.

Dependencies

Python 3.9.23

Installation

Install python:
- We recommend installing Miniconda or Anaconda.

Create python environment:

conda create --name MuscleMap python=3.9.23

Activate python environment:
```
conda activate MuscleMap
```
Download MuscleMap repository:
1. Using the git command line tool:
```
git clone https://github.com/MuscleMap/MuscleMap
```
2. From your browser:
  1. Open https://github.com/MuscleMap/MuscleMap in your browser
  2. Click the green <> Code ▼ button
  3. Click Download Zip
  4. Unzip the MuscleMap repository
Navigate to MuscleMap repository:
```
cd ./MuscleMap
```
Install python packages:
```
pip install .
```
To use a GPU, you need a compatible NVIDIA GPU with CUDA installed or a compatible AMD GPU with ROCM installed. You then need to install the corresponding GPU-compatible version of PyTorch v2.4.0. We recommend installing the PyTorch wheel with pip. After installing the correct GPU-compatible version of PyTorch, rerun:
```
pip install .
```
To use mm_register_to_template, you will need Spinal Cord Toolbox installed. We have only tested mm_register_to_template using Spinal Cord Toolbox Version 6.5.

Usage

Activate python environment:
```
conda activate MuscleMap
```

To run mm_segment:

mm_segment -i image.nii.gz

mm_segment uses our contrast agnostic whole-body segmentation model by default with currently 89 muscles and bones.

Click to see the current segmentations with labels.

```
  Left Levator Scapulae 1101
  Right Levator Scapulae 1102
  Left Semispinalis Cervicis And Multifidus 1111
  Right Semispinalis Cervicis And Multifidus 1112
  Left Semispinalis Capitis 1121
  Right Semispinalis Capitis 1122
  Left Splenius Capitis 1131
  Right Splenius Capitis 1132
  Left Sternocleidomastoid 1141
  Right Sternocleidomastoid 1142
  Left Longus Colli 1151
  Right Longus Colli 1152
  Left Trapezius 1161
  Right Trapezius 1162
  Left Supraspinatus 2101
  Right Supraspinatus 2102
  Left Subscapularis 2111
  Right Subscapularis 2112
  Left Infraspinatus 2121
  Right Infraspinatus 2122
  Left Deltoid 2141
  Right Deltoid 2142
  Left Rhomboid 4101
  Right Rhomboid 4102
  Left Thoracolumbar Multifidus 5101
  Right Thoracolumbar Multifidus 5102
  Left Erector Spinae 5111
  Right Erector Spinae 5112
  Left Psoas Major 5121
  Right Psoas Major 5122
  Left Quadratus Lumborum 5131
  Right Quadratus Lumborum 5132
  Left Lattisimus Dorsi 5141
  Right Lattisimus Dorsi 5142
  Left Gluteus Minimus 6101
  Right Gluteus Minimus 6102
  Left Gluteus Medius 6111
  Right Gluteus Medius 6112
  Left Gluteus Maximus 6121
  Right Gluteus Maximus 6122
  Left Tensor Fascia Latae 6131
  Right Tensor Fascia Latae 6132
  Left Iliacus 6141
  Right Iliacus 6142
  Left Ilium 6151
  Right Ilium 6152
  Sacrum 6160
  Left Femur 6171
  Right Femur 6172
  Left Vastus Lateralis 7101
  Right Vastus Lateralis 7102
  Left Vastus Intermedius 7111
  Right Vastus Intermedius 7112
  Left Vastus Medialis 7121
  Right Vastus Medialis 7122
  Left Rectus Femoris 7131
  Right Rectus Femoris 7132
  Left Sartorius 7141
  Right Sartorius 7142
  Left Gracilis 7151
  Right Gracilis 7152
  Left Semimembranosus 7161
  Right Semimembranosus 7162
  Left Semitendinosus 7171
  Right Semitendinosus 7172
  Left Biceps Femoris Long Head 7181
  Right Biceps Femoris Long Head 7182
  Left Biceps Femoris Short Head 7191
  Right Biceps Femoris Short Head 7192
  Left Adductor Magnus 7201
  Right Adductor Magnus 7202
  Left Adductor Longus 7211
  Right Adductor Longus 7212
  Left Adductor Brevis 7221
  Right Adductor Brevis 7222
  Left Anterior Compartment 8101
  Right Anterior Compartment 8102
  Left Deep Posterior Compartment 8111
  Right Deep Posterior Compartment 8112
  Left Lateral Compartment 8121
  Right Lateral Compartment 8122
  Left Soleus 8131
  Right Soleus 8132
  Left Gastrocnemius 8141
  Right Gastrocnemius 8142
  Left Tibia 8151
  Right Tibia 8152
  Left Fibula 8161
  Right Fibula 8162
```

The default spatial overlap during sliding window inference is 90%. If inference speed needs to be increased, the spatial overlap can be lowered. For large high-resolution or whole-body images, we recommend lowering the spatial inference to 50%:
```
mm_segment -i image.nii.gz -s 50
```
Users may specify our legacy region segmentation models (version 0.0) with -r.
- Available regions: abdomen, pelvis, thigh, and leg.
mm_segment will use GPU if detected. Users can force mm_segment to use CPU with -g N.
Run mm_segment -h to see all available options.
We are continuously expanding the whole-body model. We are working on adding the arm, forearm, hand, abdomen, spine, hip rotators, pelvic floor, and foot. If you have an immediate need, please open an issue.

We highly recommend visualizing and manually correcting the segmentations for errors. We use ITK-SNAP and Slicer, which are free and open-source.

If the models do not work well on your images, please open an issue. If you share your images, we will update the MuscleMap segmentation model to improve its accuracy on your images.

To run mm_extract_metrics:
1. For T1w and T2w MRI:
```
mm_extract_metrics -m gmm -r wholebody -i image.nii.gz -s image_dseg.nii.gz -c 3
```
- Users may specify Gaussian mixture modeling (gmm) or kmeans clustering (kmeans) with -m.
- Users may specify 2 or 3 components with -c.
- For gmm, probability maps are ouput for each component and label (*_softseg.nii.gz).
- For gmm and kmeans, binarized segmentations are ouput for each component and label (*_seg.nii.gz).
1. For Dixon Fat-Water MRI:
```
mm_extract_metrics -m dixon -r wholebody -f image_fat.nii.gz -w image_water.nii.gz -s image_dseg.nii.gz
```
2. For Dixon Fat Fraction MRI or CT:
```
mm_extract_metrics -m average -r wholebody -i image.nii.gz -s image_dseg.nii.gz
```
To run mm_segment and mm_extract_metrics via a graphical user interface (GUI):
```
mm_gui
```
- To automatically run mm_segment followed by mm_extract metrics use the chaining options in the GUI.
To run mm_register_to_template:
```
mm_register_to_template -i image.nii.gz -s image_dseg.nii.gz -r abdomen
```
Regions
- Abdomen
  - Left and right multifidus, erector spinae, psoas major, and quadratus lumborum
Regions in development: neck, shoulder, arm, forearm, thorax, pelvis, thigh, leg, and foot

Citing MuscleMap

When citing MuscleMap, please cite the following publication:

McKay MJ, Weber KA 2nd, Wesselink EO, Smith ZA, Abbott R, Anderson DB, Ashton-James CE, Atyeo J, Beach AJ, Burns J, Clarke S, Collins NJ, Coppieters MW, Cornwall J, Crawford RJ, De Martino E, Dunn AG, Eyles JP, Feng HJ, Fortin M, Franettovich Smith MM, Galloway G, Gandomkar Z, Glastras S, Henderson LA, Hides JA, Hiller CE, Hilmer SN, Hoggarth MA, Kim B, Lal N, LaPorta L, Magnussen JS, Maloney S, March L, Nackley AG, O'Leary SP, Peolsson A, Perraton Z, Pool-Goudzwaard AL, Schnitzler M, Seitz AL, Semciw AI, Sheard PW, Smith AC, Snodgrass SJ, Sullivan J, Tran V, Valentin S, Walton DM, Wishart LR, Elliott JM. MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. J Imaging. 2024;10(11):262. https://doi.org/10.3390/jimaging10110262

When using the MuscleMap Toolbox, please cite the following publications:

mm_segment

Whole-Body

Wesselink EO, Elliott JM, McKay M, de Martino E, Caplan N, Mackey S, Cohen-Adad J, Bédard S, De Leener B, Naga Karthik E, Law CSW, Fortin M, Vleggeert–Lankamp C, Di Leva A, Kim B, Hancock M, Pool-Goudzwaard A, Pevenage P, Weber II KA. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT, abstract accepted for oral presentation at the International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition 2025, Honolulu, Hawaii, USA.

Abdomen

Wesselink EO, Elliott JM, McKay M, de Martino E, Caplan N, Mackey S, Cohen-Adad J, Bédard S, De Leener B, Naga Karthik E, Law CSW, Fortin M, Vleggeert–Lankamp C, Di Leva A, Kim B, Hancock M, Pool-Goudzwaard A, Pevenage P, Weber II KA. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT, abstract accepted for oral presentation at the International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition 2025, Honolulu, Hawaii, USA.
Wesselink EO, Pool-Goudzwaard A, De Leener B, Law CSW, Fenyo MB, Ello GM, Coppieters MW, Elliott JM, Mackey S, Weber KA 2nd. Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study. Spine J. 2024;24(7):1253-1266. https://doi.org/10.1016/j.spinee.2024.02.013
Wesselink EO, Elliott JM, Coppieters MW, Hancock MJ, Cronin B, Pool-Goudzwaard A, Weber II KA.Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain. Sci Rep. 2022;12(1):13485. https://doi.org/10.1038/s41598-022-16710-5

Pelvis

Wesselink EO, Elliott JM, McKay M, de Martino E, Caplan N, Mackey S, Cohen-Adad J, Bédard S, De Leener B, Naga Karthik E, Law CSW, Fortin M, Vleggeert–Lankamp C, Di Leva A, Kim B, Hancock M, Pool-Goudzwaard A, Pevenage P, Weber II KA. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT, abstract accepted for oral presentation at the International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition 2025, Honolulu, Hawaii, USA.
Stewart C, Wesselink EO, Perraton Z, Weber II KA, King MG, Kemp JL, Mentiplay BF, Crossley KM, Elliott JM, Heerey JJ, Scholes MJ, Lawrenson PR, Calabrese C, Semciw AI. Muscle fat and volume differences in people with hip-related pain compared to controls: A machine learning approach, Journal of Cachexia, Sarcopenia and Muscle, 2024;15(6):2642-2650.

Thigh

Wesselink EO, Elliott JM, McKay M, de Martino E, Caplan N, Mackey S, Cohen-Adad J, Bédard S, De Leener B, Naga Karthik E, Law CSW, Fortin M, Vleggeert–Lankamp C, Di Leva A, Kim B, Hancock M, Pool-Goudzwaard A, Pevenage P, Weber II KA. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT, abstract accepted for oral presentation at the International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition 2025, Honolulu, Hawaii, USA.

Leg

Wesselink EO, Elliott JM, McKay M, de Martino E, Caplan N, Mackey S, Cohen-Adad J, Bédard S, De Leener B, Naga Karthik E, Law CSW, Fortin M, Vleggeert–Lankamp C, Di Leva A, Kim B, Hancock M, Pool-Goudzwaard A, Pevenage P, Weber II KA. Segment-Any-Muscle: Towards an Open-Source, Contrast-Agnostic Computer-Vision Muscle Segmentation Model for MRI and CT, abstract accepted for oral presentation at the International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition 2025, Honolulu, Hawaii, USA.
Smith AC, Muñoz Laguna J, Wesselink EO, Scott ZE, Jenkins H, Thornton W, Wasielewski M, Connor J, Delp S, Chaudhari A, Parrish TB, Mackey S, Elliott JM, Weber II KA. Leg Muscle Volume, Intramuscular Fat, and Force Generation: Insights from a Computer Vision Model and Fat-Water MRI, Journal of Cachexia, Sarcopenia and Muscle, 2025;16(1):e13735. https://doi.org/10.1002/jcsm.13735

mm_extract_metric

Wesselink EO, Elliott JM, Pool-Goudzwaard A, Coppieters MW, Pevenage PP, Di Ieva A, Weber II KA. Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models. N Am Spine Soc J. 2024;17:100313. https://doi.org/10.1016/j.xnsj.2024.100313

mm_register_to_template

Weber KA 2nd, Wesselink EO, Gutierrez J, Law CSW, Mackey S, Ratliff J, Hu S, Chaudhari AS, Pool-Goudzwaard A, Coppieters MW, Elliott JM, Hancock M, De Leener B. Three-dimensional spatial distribution of lumbar paraspinal intramuscular fat revealed by spatial parametric mapping. Eur Spine J. 2025;34(1):27-35. https://doi.org/10.1007/s00586-024-08559-1
De Leener B, Lévy S, Dupont SM, Fonov VS, Stikov N, Louis Collins D, Callot V, Cohen-Adad J. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage. 2017;145(Pt A):24-43. https://doi.org/10.1016/j.neuroimage.2016.10.009

Publications

2025

Wesselink EO, Verheijen E, Djuric N, Coppieters M, Elliott J, Weber KA 2nd, Wouter M, Vleggeert-Lankamp C, Pool-Goudzwaard A. Lumbar Multifidus Intramuscular fat Concentrations are Associated With Recovery Following Decompressive Surgery for Lumbar Spinal Stenosis. A Longitudinal Cohort Study With 5-year Follow-up. Spine, In Press https://doi.org/10.1097/brs.0000000000005408
Smith AC, Muñoz Laguna J, Wesselink EO, Scott ZE, Jenkins H, Thornton W, Wasielewski M, Connor J, Delp S, Chaudhari A, Parrish TB, Mackey S, Elliott JM, Weber II KA. Leg Muscle Volume, Intramuscular Fat, and Force Generation: Insights from a Computer Vision Model and Fat-Water MRI, Journal of Cachexia, Sarcopenia and Muscle, 2025;16(1):e13735. https://doi.org/10.1002/jcsm.13735
Kim B, Gandomkar Z, McKay MJ, Seitz AL, Wesselink EO, Cass B, Young AA, Linklater JM, Szajer J, Subbiah K, Elliott JM, Weber KA 2nd. Developing a three-dimensional convolutional neural network for full volume auto-segmentation of shoulder Dixon MRI with comparison to Goutallier classification and two-dimensional muscle quality assessment. J Shoulder Elbow Surg, In Press. https://doi.org/10.1016/j.jse.2024.12.033
Weber KA 2nd, Wesselink EO, Gutierrez J, Law CSW, Mackey S, Ratliff J, Hu S, Chaudhari AS, Pool-Goudzwaard A, Coppieters MW, Elliott JM, Hancock M, De Leener B. Three-dimensional spatial distribution of lumbar paraspinal intramuscular fat revealed by spatial parametric mapping. Eur Spine J. 2025;34(1):27-35. https://doi.org/10.1007/s00586-024-08559-1
Wesselink EO, Hides J, Elliott JM, Hoggarth M, Weber KA 2nd, Salomoni SE, Tran V, Lindsay K, Hughes L, Weber T, Scott J, Hodges PW, Caplan N, De Martino E. New insights into the impact of bed rest on lumbopelvic muscles: A computer-vision model approach to measure fat fraction changes. J Appl Physiol, 2025;138(1):157-168. https://doi.org/10.1152/japplphysiol.00502.2024

2024

Stewart C, Wesselink EO, Perraton Z, Weber KA 2nd, King MG, Kemp JL, Mentiplay BF, Crossley KM, Elliott JM, Heerey JJ, Scholes MJ, Lawrenson PR, Calabrese C, Semciw AI. Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach. J Cachexia Sarcopenia Muscle, 2024;15(6):2642-2650. https://doi.org/10.1002/jcsm.13608
McKay MJ, Weber KA 2nd, Wesselink EO, Smith ZA, Abbott R, Anderson DB, Ashton-James CE, Atyeo J, Beach AJ, Burns J, Clarke S, Collins NJ, Coppieters MW, Cornwall J, Crawford RJ, De Martino E, Dunn AG, Eyles JP, Feng HJ, Fortin M, Franettovich Smith MM, Galloway G, Gandomkar Z, Glastras S, Henderson LA, Hides JA, Hiller CE, Hilmer SN, Hoggarth MA, Kim B, Lal N, LaPorta L, Magnussen JS, Maloney S, March L, Nackley AG, O'Leary SP, Peolsson A, Perraton Z, Pool-Goudzwaard AL, Schnitzler M, Seitz AL, Semciw AI, Sheard PW, Smith AC, Snodgrass SJ, Sullivan J, Tran V, Valentin S, Walton DM, Wishart LR, Elliott JM. MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle. J Imaging. 2024;10(11):262. https://doi.org/10.3390/jimaging10110262
Wesselink EO, Pool-Goudzwaard A, De Leener B, Law CSW, Fenyo MB, Ello GM, Coppieters MW, Elliott JM, Mackey S, Weber KA 2nd. Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study. Spine J. 2024;24(7):1253-1266. https://doi.org/10.1016/j.spinee.2024.02.013
Wesselink EO, Elliott JM, Pool-Goudzwaard A, Coppieters MW, Pevenage PP, Di Ieva A, Weber II KA. Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models. N Am Spine Soc J. 2024;17:100313. https://doi.org/10.1016/j.xnsj.2024.100313
Perraton Z, Mosler AB, Lawrenson PR, Weber II K, Elliott JM, Wesselink EO, Crossley KM, Kemp JL, Stewart C, Girdwood M, King MG, Heerey JJ, Scholes MJ, Mentiplay BF, Semciw AI. The association between lateral hip muscle size/intramuscular fat infiltration and hip strength in active young adults with long standing hip/groin pain. Phys Ther Sport. 2024;65:95-101. https://doi.org/10.1016/j.ptsp.2023.11.007
Snodgrass SJ, Weber KA 2nd, Wesselink EO, Stanwell P, Elliott JM. Reduced Cervical Muscle Fat Infiltrate Is Associated with Self-Reported Recovery from Chronic Idiopathic Neck Pain Over Six Months: A Magnetic Resonance Imaging Longitudinal Cohort Study. J Clin Med. 2024;13(15):4485. https://doi.org/10.3390/jcm13154485

2023

Wesselink EO, Pool JJM, Mollema J, Weber KA 2nd, Elliott JM, Coppieters MW, Pool-Goudzwaard AL. Is fatty infiltration in paraspinal muscles reversible with exercise in people with low back pain? A systematic review. Eur Spine J. 2023;32(3):787-796. https://doi.org/10.1007/s00586-022-07471-w

2022

Wesselink EO, Elliott JM, Coppieters MW, Hancock MJ, Cronin B, Pool-Goudzwaard A, Weber II KA.Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain. Sci Rep. 2022;12(1):13485. https://doi.org/10.1038/s41598-022-16710-5
Bodkin SG, Smith AC, Bergman BC, Huo D, Weber KA, Zarini S, Kahn D, Garfield A, Macias E, Harris-Love MO. Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults. Front Rehabil Sci. 2022;3:808538. https://doi.org/10.3389/fresc.2022.808538
Snodgrass SJ, Stanwell P, Weber KA, Shepherd S, Kennedy O, Thompson HJ, Elliott JM. Greater muscle volume and muscle fat infiltrate in the deep cervical spine extensor muscles (multifidus with semispinalis cervicis) in individuals with chronic idiopathic neck pain compared to age and sex-matched asymptomatic controls: a cross-sectional study. BMC Musculoskelet Disord. 2022;23(1):973. https://doi.org/10.1186/s12891-022-05924-3
Franettovich Smith MM, Mendis MD, Weber KA 2nd, Elliott JM, Ho R, Wilkes MJ, Collins NJ. Improving the measurement of intrinsic foot muscle morphology and composition from high-field (7T) magnetic resonance imaging. J Biomech. 2022;140:111164. https://doi.org/10.1016/j.jbiomech.2022.111164
Perraton Z, Lawrenson P, Mosler AB, Elliott JM, Weber KA 2nd, Flack NA, Cornwall J, Crawford RJ, Stewart C, Semciw AI. Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: a scoping review of lateral hip musculature. BMC Musculoskelet Disord. 2022;23(1):533. https://doi.org/10.1186/s12891-022-05439-x

2021

Paliwal M, Weber KA 2nd, Smith AC, Elliott JM, Muhammad F, Dahdaleh NS, Bodurka J, Dhaher Y, Parrish TB, Mackey S, Smith ZA. Fatty infiltration in cervical flexors and extensors in patients with degenerative cervical myelopathy using a multi-muscle segmentation model. PLoS One. 2021;16(6):e0253863. https://doi.org/10.1371/journal.pone.0253863
Weber KA 2nd, Abbott R, Bojilov V, Smith AC, Wasielewski M, Hastie TJ, Parrish TB, Mackey S, Elliott JM. Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions. Sci Rep. 2021;11(1):16567. https://doi.org/10.1038/s41598-021-95972-x

2020

Elliott JM, Smith AC, Hoggarth MA, Albin SR, Weber KA 2nd, Haager M, Fundaun J, Wasielewski M, Courtney DM, Parrish TB. Muscle fat infiltration following whiplash: A computed tomography and magnetic resonance imaging comparison. PLoS One. 2020;15(6):e0234061. https://doi.org/10.1371/journal.pone.0234061
Franettovich Smith MM, Collins NJ, Mellor R, Grimaldi A, Elliott J, Hoggarth M, Weber II KA, Vicenzino B. Foot exercise plus education versus wait and see for the treatment of plantar heel pain (FEET trial): a protocol for a feasibility study. J Foot Ankle Res. 2020;13(1):20. https://doi.org/10.1186/s13047-020-00384-1

2019

Weber KA, Smith AC, Wasielewski M, Eghtesad K, Upadhyayula PA, Wintermark M, Hastie TJ, Parrish TB, Mackey S, Elliott JM. Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury. Sci Rep. 2019;9(1):7973. https://doi.org/10.1038/s41598-019-44416-8

2017

Smith AC, Weber KA, Parrish TB, Hornby TG, Tysseling VM, McPherson JG, Wasielewski M, Elliott JM. Ambulatory function in motor incomplete spinal cord injury: a magnetic resonance imaging study of spinal cord edema and lower extremity muscle morphometry. Spinal Cord. 2017;55(7):672-678. https://doi.org/10.1038/sc.2017.18

Name		Name	Last commit message	Last commit date
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scripts		scripts
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
logo.png		logo.png
requirements.txt		requirements.txt
setup.py		setup.py
version.txt		version.txt

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MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

Planned MuscleMap Phases

Standardized Acquisition Protocol

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Convert DICOM to BIDS

MuscleMap Toolbox

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MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

Planned MuscleMap Phases

Standardized Acquisition Protocol

Data Curation

Convert DICOM to BIDS

MuscleMap Toolbox

Dependencies

Installation

Usage

Regions

Citing MuscleMap

mm_segment

Whole-Body

Abdomen

Pelvis

Thigh

Leg

mm_extract_metric

mm_register_to_template

Publications

2025

2024

2023

2022

2021

2020

2019

2017

About

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