lacan · April 25, 2023 10:44 · Apr 25, 2023 · Apr 25, 2023
diff --git a/Cluster detections by distance and different classes.groovy b/Cluster detections by distance and different classes.groovy
@@ -1,5 +1,6 @@
 // Define Clusters based on distance and from cells with different classifications
-// This script generally works for images wehre multiple cell signals are segmented as detections
+// This script works for images wehre multiple cell signals are segmented as detections
+// The detections must be children of annotations objects
 // And then a clustering step is used to get multi-classification cells
 // The clustering results are provided as circles withg name "Cluster" so you can filter from the original detections
 // Works in QuPAth 0.4.3
@@ -8,9 +9,11 @@
 // @date 20230425
 
 
-// Distance for nearest neighgor clustering, in microns
+// Distance for nearest neighbour clustering, in microns
 def distance = 10
 
+// START OF SCRIPT
+
 // Remove older clusters
 def oldClusters = getAllObjects().findAll { it.getROI() instanceof EllipseROI }
 removeObjects( oldClusters, false )

diff --git a/Cluster detections by distance and different classes.groovy b/Cluster detections by distance and different classes.groovy
@@ -0,0 +1,55 @@
+// Define Clusters based on distance and from cells with different classifications
+// This script generally works for images wehre multiple cell signals are segmented as detections
+// And then a clustering step is used to get multi-classification cells
+// The clustering results are provided as circles withg name "Cluster" so you can filter from the original detections
+// Works in QuPAth 0.4.3
+//
+// @author Olivier Burri
+// @date 20230425
+
+
+// Distance for nearest neighgor clustering, in microns
+def distance = 10
+
+// Remove older clusters
+def oldClusters = getAllObjects().findAll { it.getROI() instanceof EllipseROI }
+removeObjects( oldClusters, false )
+
+// Work per annotation to keep hierarchy
+getAnnotationObjects().each{ annotation ->
+
+    def cells = annotation.getChildObjects().findAll{ it.getPathClass() != getPathClass("Ignore*") }
+
+    def delaunay = DelaunayTools.newBuilder( cells )
+                                .calibration( getCurrentServer().getPixelCalibration() )
+                                .centroids()
+                                .build()
+
+    // Make clusters based on distance and make sure that you cannot cluster cells with the same class
+    def clusters = delaunay.getClusters( DelaunayTools.centroidDistancePredicate( distance, true ).and( DelaunayTools.sameClassificationPredicate().negate() ) )
+
+
+    // Create the clusters on the dataset
+    def averaged = clusters.collect{ current ->
+
+        // Compute average position
+        def meanX = current.collect{ it.getROI().getCentroidX() }.sum() / current.size()
+        def meanY = current.collect{ it.getROI().getCentroidY() }.sum() / current.size()
+
+        def roi = ROIs.createEllipseROI( meanX-distance/2, meanY-distance/2, distance, distance, null)
+
+        // Get Path Class
+        def pathClass = PathClass.fromCollection( current.collect{ it.getPathClass().getName() }.toUnique().toSorted() )
+        def object = PathObjects.createDetectionObject( roi, pathClass )
+
+        // Help identify this object for results export and filtering later. 
+        object.setName( "Cluster" )
+        return object
+    }
+    // Add them to the annotation
+    annotation.addChildObjects( averaged )
+}
+
+fireHierarchyUpdate()
+
+import qupath.lib.roi.EllipseROI
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