thigh-us-segmentation 0.1.4

Pipeline for thigh ultrasound segmentation using nnU-Net and feature extraction

ThighUSSegmentation

Python library for automatic thigh ultrasound segmentation and analysis using nnU-Net.

Features

• Automatic image conversion to .mha
• Preprocessing (active region detection)
• nnU-Net inference
• Muscle thickness computation
• Radiomics feature extraction
• Export of anatomical landmarks (.mrk.json)
• Automatic model download from Zenodo

Installation

pip install thigh-us-segmentation

Install PyRadiomics for texture extraction

If you want to use the texture extraction feature, you need to install the PyRadiomics package separately, as it is not included in the main package:

pip install pyradiomics=="3.0.1"

Usage

from ThighUSSegmentation import run_full_pipeline

result = run_full_pipeline(
    input_image_path="image.dcm",
    output_root="outputs",
    case_id = "case001"
)

print(result["df_results"])

# OR
result = run_full_pipeline(
    input_image_path="image.mha",
    output_root="outputs",
    models_root="D:/mis_modelos", #You can also specify your own model path.
)

Output

outputs/
└── case001/
    ├── image_converted.mha
    ├── image_preprocessed.mha
    ├── case001_labelmap.mha
    ├── markups/
    └── inference_log.txt

Returned Results

The pipeline returns a dictionary:

{ "case_id": str, "segmentation_path": str, "mrk_paths": dict, "df_distances": pd.DataFrame, "df_textures": pd.DataFrame, "df_results": pd.DataFrame }

Model

The pretrained nnU-Net model is automatically downloaded from Zenodo on first use.

Expected strucutre:

Dataset001_ThighUS/
└── nnUNetTrainer__nnUNetPlans__2d/
    ├── dataset.json
    ├── plans.json
    ├── fold_0/
    ├── fold_1/
    ├── fold_2/
    ├── fold_3/
    └── fold_4/

Requirements

· Python ≥ 3.9 · SimpleITK · PyRadiomics · OpenCV · NumPy / SciPy / Pandas

Citation

If you use this work, please cite:

Isensee et al., nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods (2021)

Mara Concepción Alavarez. (2026). Thigh Ultrasound Segmentation Model (nnU-Net). Zenodo. https://doi.org/10.5281/zenodo.19914473

License

This project uses the following licenses: · Code: MIT License (or the one you choose) · Model weights (Zenodo): CC-BY 4.0