thigh-us-segmentation 0.1.0

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

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