125 lines
3.8 KiB
Markdown
125 lines
3.8 KiB
Markdown
Mammography plugin for Orthanc
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==============================
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This plugin for [Orthanc](https://orthanc.uclouvain.be/) adds a **deep
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learning algorithm to detect masses in scanned mammograms**. The inference
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algorithm produces DICOM-SR (structured reports) instances that are
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stored inside Orthanc. The DICOM-SR instance can then be displayed
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using a version of the [Stone Web viewer for
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Orthanc](https://www.orthanc-server.com/static.php?page=stone-web-viewer).
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[Reference paper](https://ebooks.iospress.nl/doi/10.3233/SHTI240603),
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in the BibTeX format:
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```BibTeX
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@inproceedings{OrthancMIE2024,
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title = {Integrated and Interoperable Platform for Detecting Masses on Mammograms},
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author = {Chatzopoulos, Edouard and Jodogne, S\'ebastien},
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booktitle = {34th Medical Informatics Europe Conference (MIE 2024)},
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address = {Athens, Greece},
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journal = {Studies in Health Technology and Informatics},
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volume = {316},
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pages = {1103-1107},
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issn = {1879-8365},
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doi = {10.3233/SHTI240603},
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publisher = {{IOS} Press},
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year = {2024}
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}
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```
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This plugin is copyrighted by the UCLouvain university, under the
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AGPLv3+ license. It is a deliverable of the research lab led by
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[Sébastien Jodogne](https://info.ucl.ac.be/~sjodogne/).
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Setup
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-----
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Make sure that Python 3 is installed with support of [virtual
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environments](https://docs.python.org/3/library/venv.html). Under
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Debian/Ubuntu distributions, this can be done by running:
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```
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$ sudo apt update
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$ sudo apt install python3 python3-venv
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```
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Evidently, you also have to [install
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Orthanc](https://www.orthanc-server.com/download.php). The mammography
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plugin requires the two following plugins for Orthanc to be installed:
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* [Python plugin](https://orthanc.uclouvain.be/book/plugins/python.html).
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* [DICOMweb plugin](https://orthanc.uclouvain.be/book/plugins/dicomweb.html), if you want to use the DICOM-SR viewer.
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Configuration
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-------------
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Firstly, clone this repository:
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```
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$ cd /tmp/
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$ git clone https://github.com/jodogne/orthanc-mammography/
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```
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Secondly, setup the Python virtual environment with the required
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dependencies:
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```
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$ cd /tmp/orthanc-mammography/
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$ python3 -m venv venv
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$ source ./venv/bin/activate
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(venv) python3 -m pip install --upgrade pip
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(venv) python3 -m pip install -r requirements.txt
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```
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Thirdly, create a [configuration
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file](https://orthanc.uclouvain.be/book/plugins/dicomweb.html#installation)
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for Orthanc with Python. A minimalist configuration file would look
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like (make sure to adapt the path to the virtual environment,
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depending on your version of Python):
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```json
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{
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"Plugins" : [
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"...list of paths to the Python and DICOMweb plugins..."
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],
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"Python" : {
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"Path" : "./mammography.py"
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},
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"Mammography" : {
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"VirtualEnv" : "./venv/lib/python3.10/site-packages/"
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}
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}
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```
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Finally, start Orthanc using this configuration file.
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Usage
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-----
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Two samples images from the [CBIS-DDSM
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dataset](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=22516629)
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are available in the [`./sample-images/` folder](./sample-images/).
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[Upload DICOM images of mammograms to
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Orthanc](https://orthanc.uclouvain.be/book/users/cookbook.html#uploading-dicom-files).
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Using the built-in [Orthanc
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Explorer](http://localhost:8042/app/explorer.html) interface of
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Orthanc, go down in the DICOM hierarchy until you reach a DICOM
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instance whose modality is `MG` (mammogram). The yellow button `Deep
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learning for mammography` will appear.
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Clicking on the button will launch the deep learning inference. Once
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the inference is done (this takes about 5 seconds on a standard
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laptop), Orthanc Explorer is automatically redirected to the
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newly-created DICOM series that contains the DICOM-SR instance with
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the results of the inference.
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You can then click on the yellow button `Stone Web viewer (for
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mammography)` to display the content of the DICOM-SR.
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