Merge branch 'master' into visualisation-and-registration-updates

.github/workflows/build_docs.yml

@@ -1,6 +1,9 @@
 name: Build Docs
 
-on: workflow_dispatch
+on:
+  workflow_dispatch:
+  push:
+    branches: ["main"]
 
 jobs:
   docs:
@@ -15,8 +18,10 @@ jobs:
       - name: Install dependencies
         run: |
           curl -sSL https://install.python-poetry.org | python - --version 1.3.2
-          poetry install --with docs
+          poetry install --with docs --all-extras
           echo "PYTHONPATH=`pwd`" >> $GITHUB_ENV
+      - name: Build Docs
+        run: |
           sudo apt-get update -y && sudo apt-get install -y pandoc python3-pkg-resources python3-setuptools
           poetry run sphinx-build -b html -a docs docs/site
       - name: Deploy docs

.github/workflows/package.yml

@@ -124,3 +124,13 @@ jobs:
           tags: |
             platipy/platipy:nnunet
             ghcr.io/pyplati/platipy:nnunet
+
+      - name: Build and Push TotalSegmentator Image
+        uses: docker/build-push-action@v2
+        with:
+          context: services/totalsegmentator
+          platforms: linux/amd64
+          push: true
+          tags: |
+            platipy/platipy:totalsegmentator
+            ghcr.io/pyplati/platipy:totalsegmentator

CITATION.cff

@@ -1,46 +1,34 @@
-cff-version: 1.2.0
-title: "PlatiPy: Processing Library and Analysis Toolkit for Medical Imaging in Python"
-message: >-
-  If you find the PlatiPy library useful in your research, please consider citing it using these
-  metadata.
-abstract: >-
-  PlatiPy is a library of tools developed in Python for image processing and analysis of medical
-  images. A broad scope of functionality is provided including image conversion, registration
-  and segmentation is provided as well as computation of similarity and radiotherapy dosimtery
-  metrics and much more.
-type: software
+cff-version: "1.2.0"
 authors:
-  - given-names: Phillip
-    family-names: Chlap
-    email: [email protected]
-    affiliation: University of New South Wales
+- family-names: Chlap
+  given-names: Phillip
+  orcid: "https://orcid.org/0000-0002-6517-8745"
+- family-names: Finnegan
+  given-names: Robert N.
+  orcid: "https://orcid.org/0000-0003-4728-8462"
+doi: 10.5281/zenodo.8032858
+message: If you use this software, please cite our article in the
+  Journal of Open Source Software.
+preferred-citation:
+  authors:
+  - family-names: Chlap
+    given-names: Phillip
     orcid: "https://orcid.org/0000-0002-6517-8745"
-  - given-names: Robert
-    family-names: Finnegan
-    email: [email protected]
-    affiliation: University of Sydney
+  - family-names: Finnegan
+    given-names: Robert N.
     orcid: "https://orcid.org/0000-0003-4728-8462"
-license: Apache-2.0
-repository-code: "https://github.com/pyplati/platipy"
-url: "https://pyplati.github.io/platipy/"
-keywords:
-  - "medical image analysis"
-  - "auto-segmentation"
-  - "image registration"
-references:
-  - authors:
-      - family-names: Lowekamp
-        given-names: Bradley C
-      - family-names: Chen
-        given-names: David T
-      - family-names: Ibáñez
-        given-names: Luis
-      - family-names: Blezek
-        given-names: Daniel
-    doi: 10.3389/fninf.2013.00045
-    issn: 16625196
-    journal: "Frontiers in Neuroinformatics"
-    title: "The design of simpleITK"
-    type: article
-    volume: 7
-    year: 2013
+  date-published: 2023-06-26
+  doi: 10.21105/joss.05374
+  issn: 2475-9066
+  issue: 86
+  journal: Journal of Open Source Software
+  publisher:
+    name: Open Journals
+  start: 5374
+  title: "PlatiPy: Processing Library and Analysis Toolkit for Medical
+    Imaging in Python"
+  type: article
+  url: "https://joss.theoj.org/papers/10.21105/joss.05374"
+  volume: 8
+title: "PlatiPy: Processing Library and Analysis Toolkit for Medical
+  Imaging in Python"

Dockerfile

@@ -8,7 +8,7 @@ RUN pip install --upgrade pip
 COPY poetry.lock /platipy/poetry.lock
 COPY pyproject.toml /platipy/pyproject.toml
 
-RUN curl -sSL https://install.python-poetry.org | python -  --version 1.2.1
+RUN curl -sSL https://install.python-poetry.org | python -  --version 1.3.2
 RUN echo 'export PATH="/root/.local/bin:$PATH"' >> ~/.bashrc
 RUN echo "/usr/lib/python3.8/site-packages" >> /usr/local/lib/python3.8/dist-packages/site-packages.pth
 

README.md

@@ -1,12 +1,15 @@
-# PlatiPy 
+# PlatiPy
+
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.05374/status.svg)](https://doi.org/10.21105/joss.05374)
+
 ## Processing Library and Analysis Toolkit for Medical Imaging in Python
 
 PlatiPy is a library of **amazing** tools for image processing and analysis - designed specifically
-for medical imaging! 
+for medical imaging!
 
 Check out the [PlatiPy documentation](https://pyplati.github.io/platipy/) for more info.
 
-This project was motivated by the need for a simple way to use, visualise, process, and analyse 
+This project was motivated by the need for a simple way to use, visualise, process, and analyse
 medical images. Many of the tools and algorithms are designed in the context of radiation therapy,
 although they are more widely applicable to other fields that use 2D, 3D, or 4D imaging.
 
@@ -16,31 +19,33 @@ We welcome feedback and contributions from the community (yes, you!) and you can
 information about contributing [here](https://pyplati.github.io/platipy/contributing.html).
 
 ## What can I do with **platipy**?
-A lot! A good place to start is by looking in the 
+
+A lot! A good place to start is by looking in the
 [examples directory](https://github.com/pyplati/platipy/tree/master/examples).
 
 Some examples of what PlatiPy can do:
- - DICOM organising and converting:
-    * Bulk convert from multiple series and studies with a single function
-    * Convert DICOM-RT structure and dose filesto NIfTI images
-    * Create DICOM-RT structure files from binary masks e.g. from automatic contouring algorithms
- - Image registration
-    * Register images and transform labels with a few lines of code
-    * Linear transformations: rigid, affine, similarity
-    * Non-linear deformable transformations: demons, b-splines
-    * Multiple metrics for optimisation
- - Atlas-based segmentation
-    * A suite of tools that can be used out-of-the-box
-    * Includes advanced algorithms for 
-      [iterative atlas selection](https://doi.org/10.1088/1361-6560/ab652a/) and 
+
+- DICOM organising and converting:
+  - Bulk convert from multiple series and studies with a single function
+  - Convert DICOM-RT structure and dose files to NIfTI images
+  - Create DICOM-RT structure files from binary masks e.g. from automatic contouring algorithms
+- Image registration
+  - Register images and transform labels with a few lines of code
+  - Linear transformations: rigid, affine, similarity
+  - Non-linear deformable transformations: demons, b-splines
+  - Multiple metrics for optimisation
+- Atlas-based segmentation
+  - A suite of tools that can be used out-of-the-box
+  - Includes advanced algorithms for
+      [iterative atlas selection](https://doi.org/10.1088/1361-6560/ab652a/) and
       [vessel splining](https://doi.org/10.1088/1361-6560/abcb1d/)
- - Synthetic deformation field generation
-    * Simulate anatomically realistic shifts, expansions, and bending
-    * Compare DIR results from clinical systems
- - Basic tools for image processing and analysis
-    * Computing label similarity metrics: DSC, mean distance to agreement, Hausdorff distance, and more
-    * Cropping images to a region of interest
-    * Rotate images and generate maximum/mean intensity projections (beams eye view modelling)
+- Synthetic deformation field generation
+  - Simulate anatomically realistic shifts, expansions, and bending
+  - Compare DIR results from clinical systems
+- Basic tools for image processing and analysis
+  - Computing label similarity metrics: DSC, mean distance to agreement, Hausdorff distance, and more
+  - Cropping images to a region of interest
+  - Rotate images and generate maximum/mean intensity projections (beams eye view modelling)
 
 A major part of this package is **visualisation**, and some examples are shown below!
 
@@ -74,7 +79,7 @@ fig = vis.show()
 ![Figure 2](assets/figure_2.png)
 
 #### Calculate deformation vector fields
-    
+
 ```python
 from platipy.imaging.registration.deformable import fast_symmetric_forces_demons_registration
 
@@ -99,6 +104,7 @@ fig = vis.show()
 ![Figure 3](assets/figure_3.png)
 
 ## Getting started
+
 There aren't many requirements, just an installed Python interpreter (3.7 or greater). PlatiPy can
 be installed with **pip**:
 
@@ -117,5 +123,5 @@ pip install platipy[backend]
 
 ## Authors
 
-* **Phillip Chlap** - [[email protected]]([email protected])
-* **Robert Finnegan** - [[email protected]]([email protected])
+- **Phillip Chlap** - [[email protected]]([email protected])
+- **Robert Finnegan** - [[email protected]]([email protected])

docs/conf.py

@@ -72,6 +72,9 @@ def setup(app):
 shutil.copy("../examples/atlas_segmentation.ipynb", "_examples/atlas_segmentation.ipynb")
 shutil.copy("../examples/contour_comparison.ipynb", "_examples/contour_comparison.ipynb")
 shutil.copy("../examples/bronchus_segmentation.ipynb", "_examples/bronchus_segmentation.ipynb")
+shutil.copy(
+    "../examples/left_ventricle_17_segments.ipynb", "_examples/left_ventricle_17_segments.ipynb"
+)
 
 shutil.rmtree("site/assets", ignore_errors=True)
 os.makedirs("site", exist_ok=True)

docs/index.rst

@@ -29,6 +29,7 @@ PlatiPy documentation
     _examples/atlas_segmentation
     _examples/contour_comparison
     _examples/dvh_analysis
+    _examples/left_ventricle_17_segments
 
 .. toctree::
     :caption: Reference

docs/utils.rst

@@ -26,6 +26,12 @@ Geometry
 .. automodule:: platipy.imaging.utils.geometry
     :members:
 
+Left Ventricle
+==============
+
+.. automodule:: platipy.imaging.utils.ventricle
+    :members:
+
 IO
 ==
 

examples/README.md

@@ -31,6 +31,10 @@
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/examples/cardiac_segmentation.ipynb)
 
+### Example to Perform Cardiac Left Ventricle 17-segment model Segmentation
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/examples/left_ventricle_17_segments.ipynb)
+
 ### Example to Perform Automatic Bronchial Tree Segmentation
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/examples/bronchus_segmentation.ipynb)