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|
%global _empty_manifest_terminate_build 0
Name: python-pydicom-seg
Version: 0.4.1
Release: 1
Summary: Python package for DICOM-SEG medical segmentation file reading and writing
License: MIT
URL: https://github.com/razorx89/pydicom-seg
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/66/12/b6cf4dbeb111e515cbec834a7ee6f6b8da9a7a8d4962cc2b31adfc7adad5/pydicom-seg-0.4.1.tar.gz
BuildArch: noarch
Requires: python3-pydicom
Requires: python3-SimpleITK
Requires: python3-numpy
Requires: python3-jsonschema
%description
# pydicom-seg
[](https://opensource.org/licenses/MIT)
[](https://img.shields.io/pypi/pyversions/pydicom-seg.svg)
[](https://badge.fury.io/py/pydicom-seg)
[](https://doi.org/10.5281/zenodo.3597420)
Reading and writing of [DICOM-SEG](http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.html) medical image segmentation storage files using [pydicom](https://github.com/pydicom/pydicom) as DICOM serialization/deserialization library. For detailed explanations about supported functionality and usage please have a look at the [documentation](https://razorx89.github.io/pydicom-seg).
## Motivation
Converting DICOM-SEG files into ITK compatible data formats, commonly used for
research, is made possible by the [dcmqi](https://github.com/QIICR/dcmqi)
project for some time. However, the project is written in C++ and offers only
access to the conversion via the binaries `itkimage2segimage` and
`segimage2itkimage`. After a conversion of a DICOM-SEG file to ITK NRRD file
format, the user has to scan the output directory for generated files, load
them individually and potentially combine multiple files to the desired format.
This library aims to make this process much easier, by providing a Python
native implementation of reading and writing functionality with support for
`numpy` and `SimpleITK`. Additionally, common use cases like loading
multi-class segmentations are supported out-of-the-box.
## Installation
### Install from PyPI
```bash
pip install pydicom-seg
```
### Install from source
This package uses [Poetry](https://python-poetry.org/) (version >= 1.0.5) as build system.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry build
pip install dist/pydicom_seg-<version>-py3-none-any.whl
```
### Development
After cloning the repository, please install the git `pre-commit` hook to
enforce code style and run static code analysis on every git commit.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry install
poetry run pre-commit install
```
## Getting Started
### Loading binary segments
```python
import pydicom
import pydicom_seg
import SimpleITK as sitk
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.SegmentReader()
result = reader.read(dcm)
for segment_number in result.available_segments:
image_data = result.segment_data(segment_number) # directly available
image = result.segment_image(segment_number) # lazy construction
sitk.WriteImage(image, f'/tmp/segmentation-{segment_number}.nrrd', True)
```
### Loading a multi-class segmentation
```python
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.MultiClassReader()
result = reader.read(dcm)
image_data = result.data # directly available
image = result.image # lazy construction
sitk.WriteImage(image, '/tmp/segmentation.nrrd', True)
```
### Saving a multi-class segmentation
Please generate a `metainfo.json` for the segments you want to serialize using the
[web-based editor from dcmqi](http://qiicr.org/dcmqi/#/seg).
```python
segmentation: SimpleITK.Image = ... # A segmentation image with integer data type
# and a single component per voxel
dicom_series_paths = [...] # Paths to an imaging series related to the segmentation
source_images = [
pydicom.dcmread(x, stop_before_pixels=True)
for x in dicom_series_paths
]
template = pydicom_seg.template.from_dcmqi_metainfo('metainfo.json')
writer = pydicom_seg.MultiClassWriter(
template=template,
inplane_cropping=False, # Crop image slices to the minimum bounding box on
# x and y axes. Maybe not supported by other frameworks.
skip_empty_slices=True, # Don't encode slices with only zeros
skip_missing_segment=False, # If a segment definition is missing in the
# template, then raise an error instead of
# skipping it.
)
dcm = writer.write(segmentation, source_images)
dcm.save_as('segmentation.dcm')
```
## License
`pydicom-seg` is distributed under the [MIT license](./LICENSE).
%package -n python3-pydicom-seg
Summary: Python package for DICOM-SEG medical segmentation file reading and writing
Provides: python-pydicom-seg
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pydicom-seg
# pydicom-seg
[](https://opensource.org/licenses/MIT)
[](https://img.shields.io/pypi/pyversions/pydicom-seg.svg)
[](https://badge.fury.io/py/pydicom-seg)
[](https://doi.org/10.5281/zenodo.3597420)
Reading and writing of [DICOM-SEG](http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.html) medical image segmentation storage files using [pydicom](https://github.com/pydicom/pydicom) as DICOM serialization/deserialization library. For detailed explanations about supported functionality and usage please have a look at the [documentation](https://razorx89.github.io/pydicom-seg).
## Motivation
Converting DICOM-SEG files into ITK compatible data formats, commonly used for
research, is made possible by the [dcmqi](https://github.com/QIICR/dcmqi)
project for some time. However, the project is written in C++ and offers only
access to the conversion via the binaries `itkimage2segimage` and
`segimage2itkimage`. After a conversion of a DICOM-SEG file to ITK NRRD file
format, the user has to scan the output directory for generated files, load
them individually and potentially combine multiple files to the desired format.
This library aims to make this process much easier, by providing a Python
native implementation of reading and writing functionality with support for
`numpy` and `SimpleITK`. Additionally, common use cases like loading
multi-class segmentations are supported out-of-the-box.
## Installation
### Install from PyPI
```bash
pip install pydicom-seg
```
### Install from source
This package uses [Poetry](https://python-poetry.org/) (version >= 1.0.5) as build system.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry build
pip install dist/pydicom_seg-<version>-py3-none-any.whl
```
### Development
After cloning the repository, please install the git `pre-commit` hook to
enforce code style and run static code analysis on every git commit.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry install
poetry run pre-commit install
```
## Getting Started
### Loading binary segments
```python
import pydicom
import pydicom_seg
import SimpleITK as sitk
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.SegmentReader()
result = reader.read(dcm)
for segment_number in result.available_segments:
image_data = result.segment_data(segment_number) # directly available
image = result.segment_image(segment_number) # lazy construction
sitk.WriteImage(image, f'/tmp/segmentation-{segment_number}.nrrd', True)
```
### Loading a multi-class segmentation
```python
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.MultiClassReader()
result = reader.read(dcm)
image_data = result.data # directly available
image = result.image # lazy construction
sitk.WriteImage(image, '/tmp/segmentation.nrrd', True)
```
### Saving a multi-class segmentation
Please generate a `metainfo.json` for the segments you want to serialize using the
[web-based editor from dcmqi](http://qiicr.org/dcmqi/#/seg).
```python
segmentation: SimpleITK.Image = ... # A segmentation image with integer data type
# and a single component per voxel
dicom_series_paths = [...] # Paths to an imaging series related to the segmentation
source_images = [
pydicom.dcmread(x, stop_before_pixels=True)
for x in dicom_series_paths
]
template = pydicom_seg.template.from_dcmqi_metainfo('metainfo.json')
writer = pydicom_seg.MultiClassWriter(
template=template,
inplane_cropping=False, # Crop image slices to the minimum bounding box on
# x and y axes. Maybe not supported by other frameworks.
skip_empty_slices=True, # Don't encode slices with only zeros
skip_missing_segment=False, # If a segment definition is missing in the
# template, then raise an error instead of
# skipping it.
)
dcm = writer.write(segmentation, source_images)
dcm.save_as('segmentation.dcm')
```
## License
`pydicom-seg` is distributed under the [MIT license](./LICENSE).
%package help
Summary: Development documents and examples for pydicom-seg
Provides: python3-pydicom-seg-doc
%description help
# pydicom-seg
[](https://opensource.org/licenses/MIT)
[](https://img.shields.io/pypi/pyversions/pydicom-seg.svg)
[](https://badge.fury.io/py/pydicom-seg)
[](https://doi.org/10.5281/zenodo.3597420)
Reading and writing of [DICOM-SEG](http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.html) medical image segmentation storage files using [pydicom](https://github.com/pydicom/pydicom) as DICOM serialization/deserialization library. For detailed explanations about supported functionality and usage please have a look at the [documentation](https://razorx89.github.io/pydicom-seg).
## Motivation
Converting DICOM-SEG files into ITK compatible data formats, commonly used for
research, is made possible by the [dcmqi](https://github.com/QIICR/dcmqi)
project for some time. However, the project is written in C++ and offers only
access to the conversion via the binaries `itkimage2segimage` and
`segimage2itkimage`. After a conversion of a DICOM-SEG file to ITK NRRD file
format, the user has to scan the output directory for generated files, load
them individually and potentially combine multiple files to the desired format.
This library aims to make this process much easier, by providing a Python
native implementation of reading and writing functionality with support for
`numpy` and `SimpleITK`. Additionally, common use cases like loading
multi-class segmentations are supported out-of-the-box.
## Installation
### Install from PyPI
```bash
pip install pydicom-seg
```
### Install from source
This package uses [Poetry](https://python-poetry.org/) (version >= 1.0.5) as build system.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry build
pip install dist/pydicom_seg-<version>-py3-none-any.whl
```
### Development
After cloning the repository, please install the git `pre-commit` hook to
enforce code style and run static code analysis on every git commit.
```bash
git clone \
--recurse-submodules \
https://github.com/razorx89/pydicom-seg.git
cd pydicom-seg
poetry install
poetry run pre-commit install
```
## Getting Started
### Loading binary segments
```python
import pydicom
import pydicom_seg
import SimpleITK as sitk
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.SegmentReader()
result = reader.read(dcm)
for segment_number in result.available_segments:
image_data = result.segment_data(segment_number) # directly available
image = result.segment_image(segment_number) # lazy construction
sitk.WriteImage(image, f'/tmp/segmentation-{segment_number}.nrrd', True)
```
### Loading a multi-class segmentation
```python
dcm = pydicom.dcmread('segmentation.dcm')
reader = pydicom_seg.MultiClassReader()
result = reader.read(dcm)
image_data = result.data # directly available
image = result.image # lazy construction
sitk.WriteImage(image, '/tmp/segmentation.nrrd', True)
```
### Saving a multi-class segmentation
Please generate a `metainfo.json` for the segments you want to serialize using the
[web-based editor from dcmqi](http://qiicr.org/dcmqi/#/seg).
```python
segmentation: SimpleITK.Image = ... # A segmentation image with integer data type
# and a single component per voxel
dicom_series_paths = [...] # Paths to an imaging series related to the segmentation
source_images = [
pydicom.dcmread(x, stop_before_pixels=True)
for x in dicom_series_paths
]
template = pydicom_seg.template.from_dcmqi_metainfo('metainfo.json')
writer = pydicom_seg.MultiClassWriter(
template=template,
inplane_cropping=False, # Crop image slices to the minimum bounding box on
# x and y axes. Maybe not supported by other frameworks.
skip_empty_slices=True, # Don't encode slices with only zeros
skip_missing_segment=False, # If a segment definition is missing in the
# template, then raise an error instead of
# skipping it.
)
dcm = writer.write(segmentation, source_images)
dcm.save_as('segmentation.dcm')
```
## License
`pydicom-seg` is distributed under the [MIT license](./LICENSE).
%prep
%autosetup -n pydicom-seg-0.4.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pydicom-seg -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.1-1
- Package Spec generated
|