Model Description This repository contains two nnU-Net models for automated bone metastasis detection in body CT scans, developed for the paper "Deep learning achieves expert-level bone metastasis detection on CT using multimodal reference standards" published in Radiology: Artificial Intelligence. Task 503 (Model 2): Trained on both CT-visible and CT-indeterminate bone metastases Task 504 (Model 1): Trained on CT-visible bone metastases only Both models use the 3D low-resolution nnU-Net configuration and were trained with 5-fold cross-validation on contrast-enhanced abdominal and thoracic CT scans. Model 2 generally achieves better performance and is recommended for clinical applications. Installation Install nnU-Net v1: pip install nnunet Usage Setup Environment Variables # Set the path to the downloaded model folder export RESULTS_FOLDER="/path/to/downloaded/nnUNet_trained_models" Run Inference # Set input and output directories INPUT_DIR="/path/to/your/ct/scans" OUTPUT_DIR="/path/to/output/folder" # Run prediction with Model 2 (recommended) nnUNet_predict -i ${INPUT_DIR} \ -o ${OUTPUT_DIR} \ -t 503 \ -m 3d_lowres \ -f 0 1 2 3 4 \ -tr nnUNetTrainerV2_DP \ -p nnUNetPlansv2.1 # For Model 1, change -t 503 to -t 504 Input Requirements CT scans in NIfTI format (.nii.gz) Output The model generates segmentation masks in NIfTI format, where each detected bone metastasis is labeled as a separate region. Citation If you use these models, please cite our paper: "Bone Metastasis Detection on CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation" in Radiology: Artificial Intelligence.