diff --git a/example_data/example_5/config_example5.yml b/example_data/example_5/config_example5.yml
index 19fc686..dee5aa7 100644
--- a/example_data/example_5/config_example5.yml
+++ b/example_data/example_5/config_example5.yml
@@ -7,7 +7,7 @@ Cytosol_Cellpose_TimecourseSegmentation:
     output_masks: 2
     shard_size: 4000000 # Average number of pixel per tile. 10.000 * 10.000 pixel are recommended. Can be adapted to memory and computation needs. 
     chunk_size: 50 # chunk size for chunked HDF5 storage. is needed for correct caching and high performance reading. should be left at 50.
-    cache: "."
+    cache: "/Users/sophia/Documents/GitHub/SPARCSpy/development/cache"
     lower_quantile_normalization:   0.001
     upper_quantile_normalization:   0.999
     median_filter_size:   4 # Size in pixels
@@ -23,7 +23,7 @@ Multithreaded_Cytosol_Cellpose_TimecourseSegmentation:
     shard_size: 4000000 # Average number of pixel per tile. 10.000 * 10.000 pixel are recommended. Can be adapted to memory and computation needs. 
     chunk_size: 50 # chunk size for chunked HDF5 storage. is needed for correct caching and high performance reading. should be left at 50.
     threads: 5 # number of shards / tiles segmented at the same size. should be adapted to the maximum amount allowed by memory.
-    cache: "."
+    cache: "/Users/sophia/Documents/GitHub/SPARCSpy/development/cache"
     lower_quantile_normalization:   0.001
     upper_quantile_normalization:   0.999
     median_filter_size:   4 # Size in pixels
@@ -37,7 +37,7 @@ TimecourseHDF5CellExtraction:
     compression: True
     threads: 80 # threads used in multithreading
     image_size: 128 # image size in pixel
-    cache: "."
+    cache: "/Users/sophia/Documents/GitHub/SPARCSpy/development/cache"
     hdf5_rdcc_nbytes: 5242880000 # 5gb 1024 * 1024 * 5000 
     hdf5_rdcc_w0: 1
     hdf5_rdcc_nslots: 50000
\ No newline at end of file
diff --git a/src/sparcscore/pipeline/base.py b/src/sparcscore/pipeline/base.py
index b43fc8c..9e32c0f 100644
--- a/src/sparcscore/pipeline/base.py
+++ b/src/sparcscore/pipeline/base.py
@@ -2,6 +2,7 @@
 import os
 import warnings
 import shutil
+import tempfile
 
 
 class Logable(object):
@@ -102,7 +103,7 @@ def __init__(
         self.directory = directory
         self.project_location = project_location
         self.config = config
-        
+        self.create_temp_dir()
 
     def __call__(
             self, *args, debug=None, intermediate_output=None, overwrite=None, **kwargs
@@ -140,6 +141,9 @@ def __call__(
             return x
         else:
             warnings.warn("no process method defined")
+
+        #after call is completed empty out temporary directories
+        self.clear_temp_dir()
     
     def __call_empty__(
             self, *args, debug=None, intermediate_output=None, overwrite=None, **kwargs
@@ -176,6 +180,9 @@ def __call_empty__(
         else:
             warnings.warn("no return_empty_mask method defined")
 
+        #also clear empty temp directory here
+        self.clear_temp_dir()
+
     def register_parameter(self, key, value):
         """
         Registers a new parameter by updating the configuration dictionary if the key didn't exist.
@@ -210,3 +217,30 @@ def get_directory(self):
             str: Directory path.
         """
         return self.directory
+
+    def create_temp_dir(self):
+        """
+        Create a temporary directory in the cache directory specified in the config for saving all intermediate results.
+        If "cache" not specified in the config for the method no directory will be created.
+        """
+        global TEMP_DIR_NAME #this is the global variable name used within alphabase.io.tempmmap which is required to intialize a memory mapped temp array using this code
+
+        if "cache" in self.config.keys():
+            self._tmp_dir_path = os.path.join(self.config["cache"], f"{self.__class__.__name__}_")
+            self._tmp_dir = tempfile.TemporaryDirectory(prefix = self._tmp_dir_path)
+            self.log(f"Initialized temporary directory for saving all temp results at {self._tmp_dir_path}")
+            print(f"Initialized temporary directory for saving all temp results at {self._tmp_dir_path} for {self.__class__.__name__}")
+            TEMP_DIR_NAME = self._tmp_dir.name
+        else:
+            self.log("No cache directory specified in config. Skipping temporary directory creation")
+    
+    def clear_temp_dir(self):
+        """Delete created temporary directory."""
+        
+        if "_tmp_dir" in self.__dict__.keys():
+            shutil.rmtree(self._tmp_dir)
+            self.log(f"Cleaned up temporary directory at {self._tmp_dir}")
+
+            del self._tmp_dir, self._tmp_dir_path
+        else:
+            self.log(f"Temporary directory not found, skipping cleanup")
\ No newline at end of file
diff --git a/src/sparcscore/pipeline/extraction.py b/src/sparcscore/pipeline/extraction.py
index 02e28f4..c4b1949 100644
--- a/src/sparcscore/pipeline/extraction.py
+++ b/src/sparcscore/pipeline/extraction.py
@@ -28,6 +28,7 @@
 import timeit
 
 import matplotlib.pyplot as plt
+from alphabase.io import tempmmap
 
 import _pickle as cPickle
 #to perform garbage collection
@@ -226,10 +227,6 @@ def _initialize_tempmmap_array(self, index_len = 2):
                                         self.n_channels_output,
                                         self.config["image_size"],
                                         self.config["image_size"]) 
-        
-        #import tempmmap module and reset temp folder location
-        from alphabase.io import tempmmap
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
 
         #generate container for single_cell_data
         _tmp_single_cell_data = tempmmap.array(shape = self.single_cell_data_shape, dtype = np.float16)
@@ -240,8 +237,6 @@ def _initialize_tempmmap_array(self, index_len = 2):
         else:
             #use a regulary numpy array instead of a tempmmap array to be able to save strings as well as ints
             _tmp_single_cell_index  = np.empty(self.single_cell_index_shape, dtype = "<U64") #need to use U64 here otherwise information potentially becomes truncated
-        
-        self.TEMP_DIR_NAME = TEMP_DIR_NAME
 
     def _transfer_tempmmap_to_hdf5(self):
         global _tmp_single_cell_data, _tmp_single_cell_index  
@@ -295,12 +290,8 @@ def _transfer_tempmmap_to_hdf5(self):
             #this is required to process large datasets to not run into memory issues
             for ix, i in enumerate(keep_index):
                 single_cell_data[ix] = _tmp_single_cell_data[i]
-        
-        #delete tempobjects (to cleanup directory)
-        self.log(f"Tempmmap Folder location {self.TEMP_DIR_NAME} will now be removed.")
-        shutil.rmtree(self.TEMP_DIR_NAME, ignore_errors=True)
 
-        del self.TEMP_DIR_NAME, _tmp_single_cell_data, _tmp_single_cell_index 
+        del _tmp_single_cell_data, _tmp_single_cell_index 
 
     def _get_label_info(self, arg):
         index, save_index, cell_id = arg
@@ -805,12 +796,8 @@ def _transfer_tempmmap_to_hdf5(self):
 
             hf.create_dataset('single_cell_index', data = index, dtype="uint64")           
             del index
-        
-        #delete tempobjects (to cleanup directory)
-        self.log(f"Tempmmap Folder location {self.TEMP_DIR_NAME} will now be removed.")
-        shutil.rmtree(self.TEMP_DIR_NAME, ignore_errors=True)
 
-        del _tmp_single_cell_data, _tmp_single_cell_index, keep_index, self.TEMP_DIR_NAME
+        del _tmp_single_cell_data, _tmp_single_cell_index, keep_index
         gc.collect()
 
     def _save_cell_info(self, index, cell_id, image_index, label_info, stack):
diff --git a/src/sparcscore/pipeline/filter_segmentation.py b/src/sparcscore/pipeline/filter_segmentation.py
index aba1e26..8d42a70 100644
--- a/src/sparcscore/pipeline/filter_segmentation.py
+++ b/src/sparcscore/pipeline/filter_segmentation.py
@@ -18,6 +18,7 @@
 #to perform garbage collection
 import gc
 import sys
+from alphabase.io import tempmmap
 
 class SegmentationFilter(ProcessingStep):
     """SegmentationFilter helper class used for creating workflows to filter generated segmentation masks before extraction.
@@ -38,12 +39,6 @@ def read_input_masks(self, input_path):
 
         with h5py.File(input_path, "r") as hf:
             hdf_input = hf.get("labels")
-
-            #use a memory mapped numpy array to save the input image to better utilize memory consumption
-            from alphabase.io import tempmmap
-            TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-            self.TEMP_DIR_NAME = TEMP_DIR_NAME #save for later to be able to remove cached folders
-
             input_masks = tempmmap.array(shape = hdf_input.shape, dtype = np.uint16)
             input_masks = hdf_input[:2,:, :]
 
@@ -87,10 +82,8 @@ def call_as_tile(self):
         with h5py.File(self.input_path, "r") as hf:
             hdf_input = hf.get("labels")
 
-            #use a memory mapped numpy array to save the input image to better utilize memory consumption
-            from alphabase.io import tempmmap
-            TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
+            ### use a memory mapped numpy array to save the input image to better utilize memory consumption
+            
             #calculate shape of required datacontainer
             c, _, _ = hdf_input.shape
             x1 = self.window[0].start
diff --git a/src/sparcscore/pipeline/segmentation.py b/src/sparcscore/pipeline/segmentation.py
index b801d85..ec1e72d 100644
--- a/src/sparcscore/pipeline/segmentation.py
+++ b/src/sparcscore/pipeline/segmentation.py
@@ -160,10 +160,6 @@ def call_as_shard(self):
         with h5py.File(self.input_path, "r") as hf:
             hdf_input = hf.get("channels")
 
-            #use a memory mapped numpy array to save the input image to better utilize memory consumption
-            from alphabase.io import tempmmap
-            TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
             #calculate shape of required datacontainer
             c, _, _ = hdf_input.shape
             x1 = self.window[0].start
@@ -197,7 +193,6 @@ def call_as_shard(self):
         #cleanup generated temp dir and variables
         del input_image
         gc.collect()
-        shutil.rmtree(TEMP_DIR_NAME) #remove create temp directory to cleanup directory
 
         #write out window location
         self.log(f"Writing out window location to file at {self.directory}/window.csv")
@@ -1090,8 +1085,6 @@ def call_as_shard(self):
                 except Exception:
                     self.log(traceback.format_exc())
                 self.log(f"Segmentation on index {index} completed.")
-            
-        return None
 
     def save_segmentation(
         self,
@@ -1122,9 +1115,6 @@ def save_segmentation(
         del _tmp_seg
 
     def _initialize_tempmmap_array(self):
-        #reset tempmmap dir
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-        self.TEMP_DIR_NAME = TEMP_DIR_NAME
 
         # create an empty HDF5 file prepared for using as a memory mapped temp array to save segmentation results to
         # this required when trying to segment so many images that the results can no longer fit into memory
@@ -1171,11 +1161,6 @@ def _transfer_tempmmap_to_hdf5(self):
                 dtype=dt,
             )
 
-        # delete tempobjects (to cleanup directory)
-        self.log(f"Tempmmap Folder location {self.TEMP_DIR_NAME} will now be removed.")
-        shutil.rmtree(self.TEMP_DIR_NAME, ignore_errors=True)
-
-        del _tmp_seg, self.TEMP_DIR_NAME
         gc.collect()
 
     def save_image(self, array, save_name="", cmap="magma", **kwargs):
diff --git a/src/sparcscore/pipeline/selection.py b/src/sparcscore/pipeline/selection.py
index ec4f5ce..f08a21a 100644
--- a/src/sparcscore/pipeline/selection.py
+++ b/src/sparcscore/pipeline/selection.py
@@ -118,13 +118,17 @@ class will automaticly provide the most recent segmentation forward together wit
         """
         
         self.log("Selection process started")
+
+        ## TO Do
+        #check if classes and seglookup table already exist as pickle file
+        # if not create them
+        #else load them and proceed with selection
         
         # load segmentation from hdf5
         hf = h5py.File(hdf_location, 'r')
         hdf_labels = hf.get('labels')
 
         #create memory mapped temporary array for saving the segmentation
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
         c, x, y = hdf_labels.shape
         segmentation = tempmmap.array(shape = (x, y), dtype = hdf_labels.dtype)
         segmentation = hdf_labels[self.config['segmentation_channel'],:,:]
@@ -152,7 +156,5 @@ class will automaticly provide the most recent segmentation forward together wit
         shape_collection.save(savepath)
         
         del segmentation
-        self.log(f"Tempmmap Folder location {TEMP_DIR_NAME} will now be removed.")
-        shutil.rmtree(TEMP_DIR_NAME, ignore_errors=True)
 
         self.log(f"Saved output at {savepath}")
\ No newline at end of file
diff --git a/src/sparcscore/pipeline/workflows.py b/src/sparcscore/pipeline/workflows.py
index 96a2e97..1b5157b 100644
--- a/src/sparcscore/pipeline/workflows.py
+++ b/src/sparcscore/pipeline/workflows.py
@@ -39,6 +39,7 @@
 
 # for cellpose segmentation
 from cellpose import models
+from alphabase.io import tempmmap
 
 class BaseSegmentation(Segmentation):
     def __init__(self, *args, **kwargs):
@@ -1050,9 +1051,6 @@ def cellpose_segmentation(self, input_image):
         torch.cuda.empty_cache() 
 
     def process(self, input_image):
-        from alphabase.io import tempmmap
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
         # initialize location to save masks to
         self.maps = {
             "normalized": tempmmap.array(shape = input_image.shape, dtype = float),
@@ -1169,10 +1167,6 @@ def process(self, input_image):
 
         downsampled_image_size = (2, _size[1]+pad_x[1], _size[2]+pad_y[1]) 
 
-        #initialize memory mapped numpy arrays to save results into
-        from alphabase.io import tempmmap
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
         # initialize location to save masks to
         self.maps = {
             "normalized": tempmmap.array(shape = input_image.shape, dtype = float),
@@ -1311,9 +1305,6 @@ def cellpose_segmentation(self, input_image):
 
     def process(self, input_image):
 
-        from alphabase.io import tempmmap
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
         # initialize location to save masks to
         self.maps = {
             "normalized": tempmmap.array(shape = input_image.shape, dtype = float),
@@ -1423,10 +1414,6 @@ def process(self, input_image):
 
         downsampled_image_size = (2, _size[1]+pad_x[1], _size[2]+pad_y[1]) 
 
-        #initialize memory mapped numpy arrays to save results into
-        from alphabase.io import tempmmap
-        TEMP_DIR_NAME = tempmmap.redefine_temp_location(self.config["cache"])
-
         # initialize location to save masks to
         self.maps = {
             "normalized": tempmmap.array(shape = input_image.shape, dtype = float),