General code update

.github/workflows/github-hosted-action.yml

@@ -9,12 +9,12 @@ jobs:
       max-parallel: 5
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v3
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v5
       with:
-        python-version: 3.8
+        python-version: 3.11
     - name: Install dependencies
       run: |
         pip install -e .

.github/workflows/self-hosted-action.yml

@@ -12,11 +12,11 @@ jobs:
       max-parallel: 5
 
     steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v5
       with:
-        python-version: 3.8
+        python-version: 3.11
     - name: Install dependencies
       run: |
         pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

README.md

@@ -23,17 +23,34 @@ pip install oai-analysis
 
 This repository contains open-source analysis approaches for the [Osteoarthritis Initiative (OAI)](https://nda.nih.gov/oai/) magnetic resonance image (MRI) data.
 The analysis code is largely written in Python with the help of [ITK](http://itk.org) and [VTK](http://vtk.org) for data I/O and mesh processing
-as well as [PyTorch](http://pytorch.org) for the deep learning approaches for segmentation and registration. The intial development of this work
+as well as [PyTorch](http://pytorch.org) for the deep learning approaches for segmentation and registration. The initial development of this work
 was led by [UNC Chapel Hill](http://biag.cs.unc.edu) as well as [Kitware](http://kitware.com). This work is also an outgrowth of conversations within the
 [Community for Open and Reproducible Musculoskeletal Imaging Research (JC|MSK)](https://jcmsk.github.io/). Going forward, contributions by the 
 broader community are, of course, not only welcome but encouraged.
 
-The following functionality is currently supported:
+### Main functionality
 1. **Deep Learning Segmentation**: Automatic cartilage segmentation (femoral and tibial cartilage) using a 3D UNet.
-2. **Cartilage thickness**: Extraction of femoral and tibial cartilage meshes and measuring of cartilage thickness based on a closest-point thickness estimation.
-3. **Deep Learning Atlas registration**: Registration of the carilage meshes with associated cartilage thickness to a knee atlas space via a deep registration network.
+2. **Deep Learning Atlas registration**: Registration of the cartilage meshes with associated cartilage thickness to a knee atlas space via a deep registration network for 3D images.
+3. **Cartilage thickness**: Extraction of femoral and tibial cartilage meshes and measuring of cartilage thickness based on a closest-point thickness estimation.
 4. **2D thickness mapping**: Mapping of the thickness maps to a common 2D atlas space which provides full spatial correspondence. This is achieved via unrolling (based on a cylindrical coordinate system) for the femoral cartilage and a planar projection for the tibial cartilage.
-5. **Statistical analysis**: [Longitudinal statistical analysis approaches will be added shortly]
+5. **Statistical analysis**: [Longitudinal statistical analysis approaches are planned]
+
+### Pipeline steps:
+1. Read image
+ a. Read
+ b. Put into canonical orientation
+ c. Intensity preprocess (rescale to 0-1)
+2. Segment cartilage using deep learning  
+3. Register patient image to atlas image.  
+4. Transform patient mesh into atlas space using registration transform  
+5. Split the mesh into inner and outer surfaces in atlas space
+  a. Island filtering (keep largest + islands close to atlas mesh)
+  b. Mesh smoothing
+  c. Clustering into inner and outer surface
+6. Transform the inner and outer meshes back to patient space  
+7. Measuring the thickness by computing distances between inner and outer surfaces
+8. Transfer thicknesses from patient mesh to atlas mesh by iterating over atlas vertices and finding the closest patient vertex
+9. Project atlas mesh to 2D using precomputed mapping
 
 ![OAI analysis workflow](doc_imgs/OAI_workflow.png)
 
@@ -42,13 +59,13 @@ The following functionality is currently supported:
 The analysis approaches in this repository are based on our initial [OAI Analysis](https://github.com/uncbiag/OAI_analysis) work.
 Much of the functionality of the original code-base has been ported to the *OAI Analysis 2* repository. The main differences are
 1. **Refactoring**: A significant refactoring of the code so that it makes better use of [ITK](http://itk.org) conventions as well as [VTK](http://vtk.org) for all the mesh processing needs.
-2. **ICON registration**: We switched to the new [ICON](https://github.com/uncbiag/ICON) registration approach (see manuscripts below).
+2. **uniGradICON registration**: We switched to the new [uniGradICON](https://github.com/uncbiag/uniGradICON) registration approach (see manuscripts below).
 3. **Data processing**: We improved the data processing by better handling of data objects. Whereas the previous *OAI Analysis* pipeline largely depended on reading and writing various different files, the *OAI Aanalysis 2* refactoring uses ITK and VTK objects.
 4. **Jupyter notebooks**: Better support of analysis in Jupyter notebooks.
 
 We are currently working on the following features which should be available in the near future:
 1. **Distributed processing**: Whereas *OAI Analysis* was set up for cluster computing via a simple SLURM script OAI Analysis 2 is moving toward using [Dask](https://dask.org/) to allow for parallel processing on clusters and the cloud.
-2. **Workflow management**: Whereas *OAI Analysis* used custom code to avoid recomputing results, we are switching to [Dagster](https://dagster.io/) to manage data dependencies in *OAI Analysis 2*.
+2. **Workflow management**: Whereas *OAI Analysis* used custom code to avoid recomputing results, we are switching to [Dagster](https://dagster.io/) to manage data dependencies in *OAI Analysis 2*. Current progress is [here](https://github.com/PaulHax/knee-sarg).
 3. **Distribution of analysis results**: As we are planning on not only distributing code, but also analysis results (such as segmentations, meshes, thickness maps) we are planning on supporting data access via the [Interplanetary File System (IPFS)](https://ipfs.io/).
 
 ## Development
@@ -73,35 +90,21 @@ upload test data to https://data.kitware.com/#collection/6205586c4acac99f42957ac
 
 ## Citation
 
-While we used the following stationary velocity field registration approach available in [easyReg](https://github.com/uncbiag/easyreg) for *OAI Analysis*
-[[paper]](https://biag.cs.unc.edu/publication/dblp-confcvpr-shen-hxn-19/)
+We now use registration approach available in [uniGradICON](https://github.com/uncbiag/uniGradICON) for *OAI Analysis*
+[paper](https://arxiv.org/abs/2403.05780)
 ```
-@InProceedings{Shen_2019_CVPR,
-title={Networks for joint affine and non-parametric image registration},
-author={Shen, Zhengyang and Han, Xu and Xu, Zhenlin and Niethammer, Marc},
-booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-pages={4224--4233},
-year={2019}
+@article{tian2024unigradicon,
+  title={uniGradICON: A Foundation Model for Medical Image Registration},
+  author={Tian, Lin and Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Estepar, Raul San Jose and Bouix, Sylvain and Rushmore, Richard and Niethammer, Marc},
+  journal={arXiv preprint arXiv:2403.05780},
+  year={2024}
 }
 ```
 
-we now support the [ICON](https://github.com/uncbiag/ICON) registration approach which provides a very simple registration interface and makes use of ITK images and transforms:
-[[paper]](https://biag.cs.unc.edu/publication/dblp-journalscorrabs-2105-04459/)
-```
-@InProceedings{Greer_2021_ICCV,
-author    = {Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Niethammer, Marc},
-title     = {ICON: Learning Regular Maps Through Inverse Consistency},
-booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
-month     = {October},
-year      = {2021},
-pages     = {3396-3405}
-}
-```
-
-An overview of the current analysis framework will be available as a [QMSKI](https://qmski.org/) abstract
+An overview of the analysis framework is available as a [QMSKI](https://qmski.org/) conference [abstract](https://www.researchgate.net/publication/361754685_Reproducible_Workflow_for_Visualization_and_Analysis_of_OsteoArthritis_Abnormality_Progression)
 ```
 @InProceedings{Sahu_2022_QMSKI,
-author    = {Sahu, Pranjal and Greer, Hastings and Xu, Zhenlin and Shen, Zhengyang and Bonaretti, Serena and McCormick, Matt and Niethammer, Marc},3
+author    = {Sahu, Pranjal and Greer, Hastings and Xu, Zhenlin and Shen, Zhengyang and Bonaretti, Serena and McCormick, Matt and Niethammer, Marc},
 title     = {Reproducible Workflow for Visualization and Analysis of OsteoArthritis Abnormality Progression},
 booktitle = {Proceedings of the International Workshop on Quantitative Musculoskeletal Imaging (QMSKI)},
 year      = {2022}
@@ -124,8 +127,8 @@ Results obtained by the *OAI Analysis* pipeline can be found in this manuscript
 ## Acknowledgements
 
 This work was developed with support in part from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) 
-under award numbers [1R44AR074375](https://reporter.nih.gov/search/Naf5qSR3eUStFkMfGm6KpQ/project-details/9777582) and [1R01AR072013](https://reporter.nih.gov/search/eE7eB34dVUGoY1nLF3kZNA/project-details/9368542).
+under award numbers [1R44AR074375](https://reporter.nih.gov/search/Naf5qSR3eUStFkMfGm6KpQ/project-details/9777582), [1R01AR072013](https://reporter.nih.gov/search/eE7eB34dVUGoY1nLF3kZNA/project-details/9368542), and [1R01AR082684](https://reporter.nih.gov/search/TdbftRplwU-HPeF3spFejw/project-details/10822142).
 
 ## License
 
-`oai-analysis` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
+This is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.