Added new model parameter to allow the specification of a preferred magnification level.

Author: Rui-Jesus

dicoogle/src/main/java/pt/ua/dicoogle/server/web/servlets/mlprovider/InferServlet.java

@@ -156,6 +156,25 @@ private Task<MLInference> sendWSIRequest(String provider, String modelID, String
 
         try {
             DicomMetaData dicomMetaData = this.getDicomMetadata(uid);
+            DicomMetaData baseDicomMetaData = this.getDicomMetadata(baseSopInstanceUID);
+
+            WSISopDescriptor descriptor = new WSISopDescriptor();
+            descriptor.extractData(dicomMetaData.getAttributes());
+
+            WSISopDescriptor baseDescriptor = new WSISopDescriptor();
+            baseDescriptor.extractData(baseDicomMetaData.getAttributes());
+
+            double scale = (descriptor.getTotalPixelMatrixRows() * 1.0) / baseDescriptor.getTotalPixelMatrixRows();
+
+            if(!uid.equals(baseSopInstanceUID)){
+                scaleAnnotation(annotation, scale);
+            }
+
+            // Verify dimensions of annotation, reject if too big. In the future, adopt a sliding window strategy to process large processing windows.
+            double area = annotation.getArea();
+            if(area > 16000000) // This equates to a maximum of 4000x4000 which represents in RGB an image of 48MB
+                return null;
+
             BufferedImage bi = roiExtractor.extractROI(dicomMetaData, annotation);
             predictionRequest.setRoi(bi);
             Task<MLInference> task = PluginController.getInstance().infer(provider, predictionRequest);
@@ -172,12 +191,6 @@ private Task<MLInference> sendWSIRequest(String provider, String modelID, String
 
                         // Coordinates need to be converted if we're working with WSI
                         if(!prediction.getAnnotations().isEmpty()){
-                            WSISopDescriptor descriptor = new WSISopDescriptor();
-                            descriptor.extractData(dicomMetaData.getAttributes());
-                            DicomMetaData base = this.getDicomMetadata(baseSopInstanceUID);
-                            WSISopDescriptor baseDescriptor = new WSISopDescriptor();
-                            baseDescriptor.extractData(base.getAttributes());
-                            double scale = (descriptor.getTotalPixelMatrixRows() * 1.0) / baseDescriptor.getTotalPixelMatrixRows();
                             Point2D tl = annotation.getBoundingBox().get(0);
                             convertCoordinates(prediction, tl, scale);
                         }
@@ -308,9 +321,22 @@ private DicomMetaData getDicomMetadata(String sop) throws IOException{
     private void convertCoordinates(MLInference prediction, Point2D tl, double scale){
         for(BulkAnnotation ann : prediction.getAnnotations()){
             for(Point2D p : ann.getPoints()){
-                p.setX((p.getX() + tl.getX())/scale);
-                p.setY((p.getY() + tl.getY())/scale);
+                p.setX((p.getX() + tl.getX()) * scale);
+                p.setY((p.getY() + tl.getY()) * scale);
             }
         }
     }
+
+    /**
+     * When working with WSI, it is convenient to have coordinates relative to the base of the pyramid.
+     * This method takes care of that.
+     * @param scale to transform coordinates
+     * @return the ml prediction with the converted coordinates.
+     */
+    private void scaleAnnotation(BulkAnnotation annotation, double scale){
+        for(Point2D p : annotation.getPoints()){
+            p.setX((p.getX()) * scale);
+            p.setY((p.getY()) * scale);
+        }
+    }
 }

dicoogle/src/main/java/pt/ua/dicoogle/server/web/utils/cache/WSICache.java

@@ -31,20 +31,14 @@
 import pt.ua.dicoogle.sdk.datastructs.SearchResult;
 import pt.ua.dicoogle.sdk.utils.QueryException;
 
-import java.io.BufferedInputStream;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.InputStream;
 import java.net.URI;
 import java.security.InvalidParameterException;
 import java.util.List;
 import java.util.concurrent.TimeUnit;
-import java.util.zip.GZIPInputStream;
 
 /**
  * Cache used to store DicomMetadata objects temporarily, as they are quite heavy to build on-demand.
  * Used only for WSI instances.
- * @author Rui Jesus <r.jesus@ua.pt>
  */
 public class WSICache extends MemoryCache<DicomMetaData>{
 

sdk/src/main/java/pt/ua/dicoogle/sdk/datastructs/dim/BulkAnnotation.java

@@ -170,4 +170,11 @@ public List<Point2D> getBoundingBox(){
         return Arrays.asList(tl, tr, bl, br);
     }
 
+    public double getArea(){
+        List<Point2D> bbox = this.getBoundingBox();
+        double width = bbox.get(1).getX() - bbox.get(0).getX();
+        double height = bbox.get(0).getY() - bbox.get(2).getY();
+        return Math.abs(width * height);
+    }
+
 }

sdk/src/main/java/pt/ua/dicoogle/sdk/mlprovider/MLModel.java

@@ -39,6 +39,12 @@ public class MLModel implements Serializable {
 
     private List<MLModelParameter> parameters;
 
+    /**
+     * A number between 1 and n that specifies the magnification level of the images this model supports.
+     * It is only useful in pathology where algorithms might be trained on lower resolution levels of the pyramid.
+     */
+    private int processMagnification;
+
     public MLModel(String name, String id) {
         this.name = name;
         this.id = id;
@@ -47,6 +53,7 @@ public MLModel(String name, String id) {
         parameters = new ArrayList<>();
         dataType = MLDataType.IMAGE;
         creationDate = new Date();
+        processMagnification = 0;
     }
 
     public MLModel(String name, String id, List<MLModelParameter> parameters){
@@ -125,4 +132,12 @@ public void removeLabel(MLlabel label){
     public void addLabel(MLlabel label){
         this.labels.add(label);
     }
+
+    public int getProcessMagnification() {
+        return processMagnification;
+    }
+
+    public void setProcessMagnification(int processMagnification) {
+        this.processMagnification = processMagnification;
+    }
 }