This package provides pre-trained U-net models for lung segmentation. For now, two models are available:

• U-net(R231): This model was trained on a large and diverse dataset that covers a wide range of visual variabiliy. The model performs segmentation on individual slices, extracts right-left lung seperately includes airpockets, tumors and effusions. The trachea will not be included in the lung segmentation.

• U-net(LTRCLobes): This model was trained on a subset of the LTRC dataset. The model performs segmentation of individual lung-lobes but yields limited performance when dense pathologies are present.

Examples of the two models applied. Left: U-net(R231), will distinguish between left and right lung and include very dense areas such as effusions (third row), tumor or severe fibrosis (fourth row) . Right: U-net(LTRLobes), will distinguish between lung lobes but will not include very dense areas.

If you use this code or one of the trained models in your work please refer to:

Johannes Hofmanninger, Forian Prayer, Jeanny Pan, Sebastian Röhrich, Helmut Prosch and Georg Langs. "Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem". 1 2020, https://arxiv.org/abs/2001.11767

This paper contains a detailed description of the dataset used, a thorough evaluation of the U-net(R231) model, and a comparison to reference methods.

pip install git+https://github.com/JoHof/lungmask

On Windows, depending on your setup, it may be necessary to install torch beforehand: https://pytorch.org

Runtime between CPU-only and GPU supported inference varies greatly. Using the GPU, processing a volume takes only several seconds, using the CPU-only will take several minutes. To make use of the GPU make sure that your torch installation has CUDA support. In case of cuda out of memory errors reduce the batchsize to 1 with the optional argument --batchsize 1

lungmask INPUT OUTPUT

If INPUT points to a file, the file will be processed. If INPUT points to a directory, the directory will be searched for DICOM series. The largest volume found (in terms of number of voxels) will be used to compute the lungmask. OUTPUT is the output filename. All ITK formats are supported.

Choose a model:
The U-net(R231) will be used as default. However, you can specify an alternative model such as LTRCLobes...

lungmask INPUT OUTPUT --modelname LTRCLobes

For additional options type:

lungmask -h

from lungmask import lungmask
import SimpleITK as sitk

input_image = sitk.ReadImage(INPUT)
segmentation = lungmask.apply(input_image)  # default model is U-net(R231)

input_image has to be a SimpleITK object.

Load an alternative model like so:

model = lungmask.get_model('unet','LTRCLobes')
segmentation = lungmask.apply(input_image, model)