mura_forearm_humerus_annotations/README.md

FleXray manual annotations for MURA forearm and humerus radiographs

Bone segmentation masks for 100 radiographs from the MURA dataset (Rajpurkar et al., 2017; https://stanfordmlgroup.github.io/competitions/mura/), one image per patient, annotated manually with polygon tools in CVAT for the FleXray paper.

• MURA_FOREARM: 50 radiographs
• MURA_HUMERUS: 50 radiographs

The MURA images are not included: the MURA Research Use Agreement does not permit redistribution. Download MURA yourself and join the masks with the manifest below.

Files

• manifest.csv: one row per annotated image. mura_relative_path is the image path relative to the MURA download (starting with MURA-v1.1/); mask_path is the matching mask; flexray_split is the train/val/test assignment used in the paper.
• <dataset>/masks/*.png: 8-bit indexed PNG at the native MURA image resolution.
• labels.json: mask value to structure name (0: background, 1: humeri, 2: radii, 3: ulnae).
• mura_to_fxr_manifest.py: builds an fxr-dataset package manifest from your MURA copy.

Build a FleXray training package

python -m pip install "flexray[train]"
python mura_to_fxr_manifest.py --mura-root /path/containing/MURA-v1.1 \
    --annotations . --dataset MURA_FOREARM --output-dir /data/mura_forearm_pkg
fxr-dataset pack /data/mura_forearm_pkg/dataset.yml /data/flexray/MURA_FOREARM

The join script reproduces the preprocessing used to train FleXray: per-image min-max normalization, zero padding to a square, and resizing to 256 x 256 (area averaging for images, nearest for masks).

License

The annotations are released under CC BY 4.0. The MURA images remain subject to the Stanford MURA Research Use Agreement.