diff --git a/src/sparcscore/pipeline/workflows.py b/src/sparcscore/pipeline/workflows.py
index d84074b..3fc6eb0 100644
--- a/src/sparcscore/pipeline/workflows.py
+++ b/src/sparcscore/pipeline/workflows.py
@@ -5,6 +5,7 @@
     MultithreadedSegmentation,
 )
 from sparcscore.processing.preprocessing import percentile_normalization, downsample_img
+from sparcscore.processing.filtering import SizeFilter
 from sparcscore.processing.utils import visualize_class
 from sparcscore.processing.segmentation import (
     segment_local_threshold,
@@ -727,6 +728,33 @@ def _finalize_segmentation_results(self):
         ).astype(np.uint32)
 
         return channels, segmentation
+    
+    def get_params_cellsize_filtering(self, type):
+
+        absolute_filter_status = False
+        
+        if "min_size" in self.config[f"{type}_segmentation"].keys():
+            min_size = self.config[f"{type}_segmentation"]["min_size"]
+            absolute_filter_status = True
+        if "max_size" in self.config[f"{type}_segmentation"].keys():
+            max_size = self.config[f"{type}_segmentation"]["max_size"]
+            absolute_filter_status = True
+        
+        if absolute_filter_status:
+            thresholds = [min_size, max_size]
+            return (thresholds, None)
+        else:
+            thresholds = None
+
+            #get confidence intervals to automatically calculate thresholds
+            if "confidence_interval" in self.config[f"{type}_segmentation"].keys():
+                confidence_interval = self.config[f"{type}_segmentation"]["confidence_interval"]
+            else:
+                #get default value
+                self.log(f"No confidence interval specified for {type} mask filtering, using default value of 0.95")
+                confidence_interval = 0.95
+            
+            return(thresholds, confidence_interval)
 
     def cellpose_segmentation(self, input_image):
         try:
@@ -777,7 +805,13 @@ def cellpose_segmentation(self, input_image):
 
         self.log(f"GPU Status for segmentation: {use_GPU}")
 
-        # check to see if the cells should be filtered within the segmentation run
+        if "filter_masks_size" in self.config.keys():
+            self.filter_size= self.config["filter_status"]
+        else:
+            #default behaviour is that it should be turned on (this gives biologically more meaningful results)
+            self.filter_size = True 
+
+        # check to see if the cells should be filtered for matching nuclei/cytosols within the segmentation run
         if "filter_status" in self.config.keys():
             self.filter_status = self.config["filter_status"]
         else:
@@ -851,7 +885,48 @@ def cellpose_segmentation(self, input_image):
             masks_nucleus_unfiltered = masks_nucleus.copy()
             masks_cytosol_unfiltered = masks_cytosol.copy()
 
-        # add step which automatically removes very small masks/masks that are unconnected (cells must be connected)
+        ######################
+        ### Perform Filtering to remove too small/too large masks if applicable
+        ######################
+
+        if self.filter_size:
+            self.log("Filtering generated nucleus and cytosol masks based on size.")
+
+            #perform filtering for nucleus size
+            thresholds, confidence_interval = self.get_params_cellsize_filtering("nucleus")
+
+            if thresholds is not None:
+                self.log(f"Performing filtering of nuclei with specified thresholds {thresholds} from config file.")
+            else:
+                self.log(f"Automatically calculating thresholds for filtering of nuclei based on a fitted normal distribution with a confidence interval of {confidence_interval * 100}%.")
+            
+            filter_nucleus = SizeFilter(
+                label="nucleus", 
+                log=True, 
+                plot_qc=self.debug, 
+                directory=self.directory,
+                confidence_interval = confidence_interval,
+                filter_threshold = thresholds
+            )
+            masks_nucleus = filter_nucleus.filter(masks_nucleus)
+
+            #perform filtering for cytosol size
+            thresholds, confidence_interval = self.get_params_cellsize_filtering("cytosol")
+
+            if thresholds is not None:
+                self.log(f"Performing filtering of cytosols with specified thresholds {thresholds} from config file.")
+            else:
+                self.log(f"Automatically calculating thresholds for filtering of cytosols based on a fitted normal distribution with a confidence interval of {confidence_interval * 100}%.")
+            
+            filter_cytosol = SizeFilter(
+                label="cytosol", 
+                log=True, 
+                plot_qc=self.debug, 
+                directory=self.directory,
+                confidence_interval=confidence_interval,
+                filter_threshold = thresholds
+            )
+            masks_cytosol = filter_cytosol.filter(masks_cytosol)
 
         if not self.filter_status:
             self.log(