diff --git a/FijiPyLib/src/main/resources/ROITools.py b/FijiPyLib/src/main/resources/ROITools.py
index e5d8b74..b217ac0 100644
--- a/FijiPyLib/src/main/resources/ROITools.py
+++ b/FijiPyLib/src/main/resources/ROITools.py
@@ -619,7 +619,7 @@ def isContained(containedROI,containingROI):
 ########################################################################
 
 # Define a function to crop out a specified region from an ROI
-def subtractROI(TotalRegion,Region2Remove,img,growth=0):
+def subtractROI(TotalRegion,Region2Remove,img,growth=0, fit = True):
     '''
     Function that will subtract one ROI from another
 
@@ -636,34 +636,49 @@ def subtractROI(TotalRegion,Region2Remove,img,growth=0):
         - growth (int): How much do you want to expand your
                         Region2Remove before subtracting from Total
                         Region (default = 0, no growth)
+        - fit (boolean): Use rectangular fit and croppedCleanedROI. Set
+                         to false for removing overlap after enlarging
+                         ROI
 
     OUTPUT Fiji ROI storing the cropped region of interest
 
     AR Jan 2022
+    AP Jun 2022: Create boolean parameter to condition rectangular fit
+    for spatial transcriptomic enlargement code written in Semi Auto
+    Segmentation plugin.
     '''
 
     # Make sure that the image is displayed
     img.show()
 
-    # Sometimes ROIs will have fuzzy edges, so it's hard to have ROIs
-    # overlap exactly. Given this, we're going to enlarge the region
-    # to remove by 2 pixels to give us wiggle room.
-    EnlargedRegion2Remove = roienlarger.enlarge(Region2Remove,growth)
+    # if fit is true, computer ovelap and fit it to a rectangular shape
+    if fit:
 
-    # Add this enlarged region to remove to our displayed image
-    img.setRoi(EnlargedRegion2Remove)
+        # Sometimes ROIs will have fuzzy edges, so it's hard to have ROIs
+        # overlap exactly. Given this, we're going to enlarge the region
+        # to remove by 2 pixels to give us wiggle room.
+        EnlargedRegion2Remove = roienlarger.enlarge(Region2Remove,growth)
 
-    # Fit this new ROI to a rectangle
-    IJ.run('Fit Rectangle')
+        # Add this enlarged region to remove to our displayed image
+        img.setRoi(EnlargedRegion2Remove)
 
-    # Grab the edited ROI
-    EnlargedRectangularRegion2Remove = img.getRoi()
+        # Fit this new ROI to a rectangle
+        IJ.run('Fit Rectangle')
+
+        # Grab the edited ROI
+        EnlargedRectangularRegion2Remove = img.getRoi()
+
+        # Compute the area where the enlarged region and the total region
+        # overlap
+        FinalRegion2Remove = getIntersectingROI([EnlargedRectangularRegion2Remove,
+                                                 TotalRegion])
+        del EnlargedRegion2Remove
+
+    else:
 
-    # Compute the area where the enlarged region and the total region
-    # overlap
-    FinalRegion2Remove = getIntersectingROI([EnlargedRectangularRegion2Remove,
-                                             TotalRegion])
-    del EnlargedRegion2Remove
+        # Just set the final region to remove equal to initially passed value
+        # Note - Region2Remove must be overlapping completely with total region
+        FinalRegion2Remove = Region2Remove
 
     # Generate shape ROIs for both the total region and the final region
     # to remove
@@ -675,9 +690,15 @@ def subtractROI(TotalRegion,Region2Remove,img,growth=0):
     # region to remove from the total region
     croppedROI = sTotalRegion.xor(sFinalRegion2Remove)
 
-    # Sometimes after cropping, a small stray piece will be left off.
-    # Let's clean up the ROI if necessary before returning the final ROI
-    croppedCleanedROI = cleanUpROI(croppedROI,img)
+    if fit:
+
+        # Sometimes after cropping, a small stray piece will be left off.
+        # Let's clean up the ROI if necessary before returning the final ROI
+        croppedCleanedROI = cleanUpROI(croppedROI,img)
+
+    else:
+
+        croppedCleanedROI = croppedROI
 
     # Remove the ROI from the image and close it
     img.deleteRoi()
diff --git a/FijiPyPlugins/src/main/resources/scripts/Plugins/Quantify_Fields_of_View/Semi_Auto_Cell_Labeling_.py b/FijiPyPlugins/src/main/resources/scripts/Plugins/Quantify_Fields_of_View/Semi_Auto_Cell_Labeling_.py
index e26b852..d486626 100644
--- a/FijiPyPlugins/src/main/resources/scripts/Plugins/Quantify_Fields_of_View/Semi_Auto_Cell_Labeling_.py
+++ b/FijiPyPlugins/src/main/resources/scripts/Plugins/Quantify_Fields_of_View/Semi_Auto_Cell_Labeling_.py
@@ -49,6 +49,8 @@
 AR Dec 2021
 AR Feb 2022 Edited output to keep track of distance between cells and
             protein/transcript expression within each cell
+AP May 2022 Add ROI expansion and puncta quantification for spatial
+            transcriptomic data.
 '''
 
 ########################################################################
@@ -104,6 +106,12 @@
 # Initialize a duplicator object
 duplicator = Duplicator()
 
+# Import ROI Enlarger so we can dilate nuclei segmentation
+from ij.plugin import RoiEnlarger
+
+# Iniitalize a roi enlarger object
+roienlarger = RoiEnlarger()
+
 # Import izip so we can iterate across multiple lists
 from itertools import izip
 
@@ -333,6 +341,52 @@
 # Compute the area of the field of view we quantified from
 [field_area,field_length_units] = ROITools.getROIArea(fieldBoundROI,editedNucSeg)
 
+########################################################################
+######### EXPAND ROI IF SPATIAL TRANSCRIPTOMIC DATA ####################
+########################################################################
+
+# Extract the units of the image 
+nucImpCalibration = nucImp.getCalibration()
+nucImpDims     = nucImp.getDimensions
+lengthUnits    = nucImpCalibration.getUnit()
+
+# Check to see if the micron symbol was used in the length units
+if u'\xb5' in lengthUnits:
+
+	# Convert the micron symbol to a u
+	lengthUnits = lengthUnits.replace(u'\xb5','u')
+
+# Prompt the user to get amount of enlargement / dilation
+unitIncrease = UIs.textFieldsUI('Specify how much you would like to extend the radius of nuclear segmented regions of interest.', ["Extend ROI radius by {} mm".format(roundedPixelArea)], ["0"])[0]
+
+# Convert field size and field overlap from physical units to pixels
+unitIncrease = int(round(nucImpCalibration.getRawX(float(unitIncrease))))
+
+# Iterate through nucROIs and dilate them by unitIncrease defined by user
+if(unitIncrease != 0):
+	psgROIs = nucROIs
+	nucROIs = [roienlarger.enlarge(nucROI,unitIncrease) for nucROI in psgROIs]
+
+# Remove resulting overlap between newly expanded ROI
+    for i in range(len(nucROIs)):
+        # Grab the current ROI to compare to rest of ROIs
+        nucROI = nucROIs[i]
+        # Get the other ROIs using a lambda filter function
+        others = [nucROIs[j] for j in range(len(nucROIs)) if j != i]
+        # Merge the others
+        other = ROITools.combineROIs(others)
+        # Get the intersection of ROIs using the intersecting function
+        intersection = ROITools.getIntersectingROI([nucROI, other])
+        # If there is overlap, then proceed to remove it
+        if intersection.getLength() > 0:
+            # subtract AND from nucROI
+            croppedROI = subtractROI(nucROI, intersection, fit = False)
+            # Combine resulting ROI with original nuclear segmentation
+            nucROIs[i] = ROITools.combineROIs([croppedROI, psgROIs[i]])
+
+    del psgROIs, croppedROI
+    del intersection, other, others, nucROI
+
 ########################################################################
 ####################### LABEL CELLS BY CELL TYPE #######################
 ########################################################################