diff --git a/src/cvpl_tools/examples/mousebrain_processing.py b/src/cvpl_tools/examples/mousebrain_processing.py
index 0f7b81a..bdf5fb7 100644
--- a/src/cvpl_tools/examples/mousebrain_processing.py
+++ b/src/cvpl_tools/examples/mousebrain_processing.py
@@ -49,7 +49,7 @@ class Subject:
 }
 ALL_SUBJECTS = list(THRESHOLD_TABLE.keys())
 
-def get_subject(SUBJECT_ID):
+def get_subject(SUBJECT_ID, SUBJECTS_DIR, NNUNET_CACHE_DIR):
     subject = Subject()
     subject.SUBJECT_ID = SUBJECT_ID
 
@@ -78,9 +78,8 @@ def get_subject(SUBJECT_ID):
     subject.OME_ZARR_PATH = OME_ZARR_PATH
     subject.BA_CHANNEL = BA_CHANNEL
 
-    FOLDER = 'C:/ProgrammingTools/ComputerVision/RobartsResearch/data/lightsheet/tmp/mousebrain_processing'
-    subject.SUBJECT_FOLDER = f'{FOLDER}/subjects/subject_{SUBJECT_ID}'  # **CHANGE THIS**
-    subject.NNUNET_CACHE_DIR = f'{FOLDER}/nnunet_250epoch_Run20241126'  # **CHANGE THIS**
+    subject.SUBJECT_FOLDER = f'{SUBJECTS_DIR}/subject_{SUBJECT_ID}'
+    subject.NNUNET_CACHE_DIR = NNUNET_CACHE_DIR
 
     subject.FIRST_DOWNSAMPLE_PATH = f'{subject.SUBJECT_FOLDER}/first_downsample.ome.zarr'
     subject.SECOND_DOWNSAMPLE_PATH = f'{subject.SUBJECT_FOLDER}/second_downsample.ome.zarr'
@@ -197,7 +196,10 @@ async def fn(dask_worker):
         if ID in ('M4A2Te3Blaze', 'o22', 'o23'):
             continue
         print(f'Starting prediction on subject {ID}')
-        subject = get_subject(ID)
+        FOLDER = 'C:/ProgrammingTools/ComputerVision/RobartsResearch/data/lightsheet/tmp/mousebrain_processing'
+        SUBJECTS_DIR = f'{FOLDER}/subjects'
+        NNUNET_CACHE_DIR = f'{FOLDER}/nnunet_250epoch_Run20241126'
+        subject = get_subject(ID, SUBJECTS_DIR, NNUNET_CACHE_DIR)
 
         main(subject=subject, run_nnunet=True, run_coiled_process=True)
         print(f'Finished predicting on subject {ID}')
diff --git a/src/cvpl_tools/nnunet/api.py b/src/cvpl_tools/nnunet/api.py
index c54e57d..ec0e6a3 100644
--- a/src/cvpl_tools/nnunet/api.py
+++ b/src/cvpl_tools/nnunet/api.py
@@ -207,11 +207,8 @@ async def alg(im, context_args):
     )
     midtime = time.time()
     print(f'forward elapsed: {midtime - stime}')
-    lc = lc.reduce(force_numpy=True)
-    ncell_list = await cc.reduce(force_numpy=True)
 
-    print(f'ending  elapsed: {time.time() - midtime}')
-    cnt = ncell_list.sum().item()
+    lc = lc.reduce(force_numpy=True)
 
     with cache_dir_fs.open('final_lc.npy', mode='wb') as fd:
         np.save(fd, lc)