Medical Image Viewer Framework

python AI service to return annotation cordinates.
The image data to input to service by reading dicom tags.

To create a new mode in the OHIF (Open Health Imaging Foundation) Viewer that calls a Python service to retrieve annotation coordinates and overlay them on the image:

1. Set Up the Python Service:
- Ensure you have a Python service that can return annotation coordinates. This service should accept an image identifier (or relevant data) and return the coordinates for annotations in a format that can be used by OHIF.

2. Modify OHIF Viewer:
- You need to create a custom mode in the OHIF Viewer. OHIF modes are essentially configurations that define how the viewer behaves and what tools are available.

3. Integrate the Python Service Call:
- Make an API call from the OHIF Viewer to the Python service to fetch the annotation coordinates.
- Overlay the received annotation coordinates on the image.

1. Detailed Steps:

1. 1. Python Service Setup

Ensure your Python service is running and capable of returning annotation coordinates. Here is a simple example using Flask:

```python
from flask import Flask, request, jsonify

app = Flask(name)

@app.route('/get_annotations', methods=['POST'])
def get_annotations():
data = request.json
image_id = data['image_id'] # Dummy coordinates, replace with actual logic
annotations = [ {'x': 100, 'y': 150, 'width': 50, 'height': 50}, {'x': 200, 'y': 250, 'width': 60, 'height': 60},
]
return jsonify(annotations)

if name == '__main__':
app.run(debug=True)
```

1. 2. Create a New Mode in OHIF Viewer

1. Clone the OHIF Viewer Repository:
```
git clone https://github.com/OHIF/Viewers.git
cd Viewers
```

2. Create a Custom Mode:
In the `platform/viewer/src/modes` directory, create a new mode file, for example, `customAnnotationsMode.js`.

3. Define the Custom Mode:

```javascript
   // customAnnotationsMode.js
   import { id } from './id';
   import { OHIF } from '@ohif/core';

const mode = {
     id: 'customAnnotations',
     routeName: 'custom',
     displayName: 'Custom Annotations Mode',
     onModeEnter: ({ servicesManager, extensionManager }) => {
       // Set up necessary tools and extensions here
     },
     onModeExit: () => {
       // Clean up if needed
     },
     // Custom behavior here
     sopClassHandlers: [
    {
         sopClassUids: [],
         getDisplaySetsFromSeries: (series, study, servicesManager) => {
           // Fetch annotations from the Python service
           const annotations = fetchAnnotations(series);
           // Overlay annotations
           overlayAnnotations(annotations);
           return series;
         },
       },
     ],
   };

async function fetchAnnotations(series) {
     const response = await fetch('http://localhost:5000/get_annotations', {
       method: 'POST',
       headers: {
         'Content-Type': 'application/json',
       },
       body: JSON.stringify({ image_id: series.imageId }),
     });
     const annotations = await response.json();
     return annotations;
   }

function overlayAnnotations(annotations) {
     annotations.forEach(annotation => {
       // Logic to overlay annotations on the viewer
       // Example:
       console.log(`Annotation: ${JSON.stringify(annotation)}`);
     });
   }

OHIF.extensionManager.registerMode(mode);
   export default mode;
   ```

4. Register the New Mode:
Update `platform/viewer/src/index.js` to include the new mode:

```javascript
   import customAnnotationsMode from './modes/customAnnotationsMode';

const appConfig = {
     modes: [customAnnotationsMode, ...otherModes],
     // Other configuration
   };

OHIF.init(appConfig);
   ```

5. Build and Run the Viewer:
Follow the build instructions in the OHIF repository to run your customized viewer.

1. 3. Deploy and Test

1. Deploy the Python service and OHIF Viewer:
- Ensure your Python service is running and accessible.
- Run the customized OHIF Viewer and navigate to your custom mode.

2. Test the Integration:
- Load an image in the OHIF Viewer.
- Verify that the viewer makes a call to the Python service and overlays the returned annotation coordinates on the image.