armcortnet
Armcortnet provides automatic segmentation of the humerus and scapula from CT scans. The deep learning model is trained to also segment out the cortical and trabecular subregions from each bone as well.
The deep learning pipeple consists of using armcrop to crop to an oriented bounding box around each humerus or scapula in the image and then a neural network based traine from the nnUNet framework segments that cropped volume. The segmetnation is then transformed back to the original coordinate system, post-processed and finally saved as a .seg.nrrd file.
Installation
Please install pytorch first before installing armcortnet. You can learn about installing pytorch from the official website here.
Then install armcortnet using pip:
pip install armcortnet
For faster oriented bounding box cropping you can replace onnxruntime with onnxruntime-gpu.
Usage
To generate a segmentation of the humerus or scapula from a CT volume, use the following:
import armcortnet
import SimpleITK as sitk
# initialize the segmentation model
model = armcortnet.Net(bone_type="scapula") # or "humerus"
# perform segmentation prediction on a CT volume
pred_segmentations = model.predict(
vol_path="path/to/input/ct.nrrd"
)
# output is a list of SimpleITK images, one for each bone_type detected in the CT
for i, pred_seg in enumerate(pred_segmentations):
# write each of the segmentations to the disk
sitk.WriteImage(pred_seg, f"scapula-{i}.seg.nrrd")
A mesh of the predicted bone can be generated using the following:
# perform mesh prediction on a CT volume, returns list of vtkPolyData objects
pred_meshes = model.predict_poly(
vol_path="path/to/input/ct.nrrd"
)
# iterate over each detected object
for i, cort_trab_polys in enumerate(pred_meshes):
# iterate over the cortical and trabecular meshes
for j, poly in enumerate(cort_trab_polys):
armcortnet.write_polydata(p, f"scapula_{i}_{j}.ply")
Output Labels
The segmentation output contains the following labels:
- 0: Background
- 1: Other adjacent bones ("i.e clavicle, radius, ulna, etc.")
- 2: Cortical region of bone of interest
- 3: Trabecular region of bone of interest
Note: label 1 is removed when post-processing is used
Models
Trained models are automatically downloaded from HuggingFace Hub (gregspangenberg/armcortnet) on first use.
How to Cite
If you use armcortnet in your research, please cite the package:
@software{armcortnet,
author = {Spangenberg, Greg},
title = {armcortnet: Automatic segmentation of humerus and scapula from CT scans},
url = {https://pypi.org/project/armcortnet/},
year = {2025}
}
1 maintainer
Metadata
1 maintainer
Release files for armcortnet 0.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| armcortnet-0.8.0.tar.gz | 17.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| armcortnet-0.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 35.5 kB
Release files / armcortnet-0.8.0.tar.gz
| Download URL | armcortnet-0.8.0.tar.gz |
|---|---|
| Size | 17.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
32d87b2f77bc5f6cb16dbe69d499a04e6eb85f457f6ed2217b84a58a2167918f
|
|
BLAKE2b-256 checksum How to use checksums |
64d695009c6ca1e69b87499c69b461d1132e35a7d42077b73db501cff1fb8404
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.10.8 {"installer":{"name":"uv","version":"0.10.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Fedora Linux","version":"43","id":"","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
Release files / armcortnet-0.8.0-py3-none-any.whl
| Download URL | armcortnet-0.8.0-py3-none-any.whl |
|---|---|
| Size | 17.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c1d79c5d9d18e4d7356889fb456889843c5fc6258d8dc913b904db7d8bcd8b08
|
|
BLAKE2b-256 checksum How to use checksums |
2f7369809e6fa276bf188b7afb8ad3f2e2a7078f7989f958a9fe99dc5200e1ff
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.10.8 {"installer":{"name":"uv","version":"0.10.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Fedora Linux","version":"43","id":"","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
