diff --git a/wrapper.py b/wrapper.py
index 08a294b..f220a2c 100644
--- a/wrapper.py
+++ b/wrapper.py
@@ -11,8 +11,8 @@
 import skimage.segmentation
 from subprocess import call
 from cytomine.models import Job
-from neubiaswg5 import CLASS_OBJSEG
-from neubiaswg5.helpers import NeubiasJob, prepare_data, upload_data, upload_metrics
+from biaflows import CLASS_OBJSEG
+from biaflows.helpers import BiaflowsJob, prepare_data, upload_data, upload_metrics
 
 
 def label_objects(img, threshold=0.9, min_size=25):
@@ -49,10 +49,10 @@ def main(argv):
     base_path = "{}".format(os.getenv("HOME")) # Mandatory for Singularity
     problem_cls = CLASS_OBJSEG
 
-    with NeubiasJob.from_cli(argv) as nj:
-        nj.job.update(status=Job.RUNNING, progress=0, statusComment="Initialisation...")
+    with BiaflowsJob.from_cli(argv) as bj:
+        bj.job.update(status=Job.RUNNING, progress=0, statusComment="Initialisation...")
         # 1. Prepare data for workflow
-        in_imgs, gt_imgs, in_path, gt_path, out_path, tmp_path = prepare_data(problem_cls, nj, **nj.flags)
+        in_imgs, gt_imgs, in_path, gt_path, out_path, tmp_path = prepare_data(problem_cls, bj, **bj.flags)
 
         temp_img = skimage.io.imread(os.path.join(in_path,"{}".format(in_imgs[0].filename)))
         if len(temp_img.shape) > 2:
@@ -62,7 +62,7 @@ def main(argv):
             convert2rgb(in_imgs)
 
         # 2. Run ilastik prediction
-        nj.job.update(progress=25, statusComment="Launching workflow...")
+        bj.job.update(progress=25, statusComment="Launching workflow...")
         shArgs = [
             "/app/ilastik/run_ilastik.sh",
             "--headless",
@@ -80,20 +80,20 @@ def main(argv):
             fn = os.path.join(tmp_path,"{}".format(image.filename))
             outfn = os.path.join(out_path,"{}".format(image.filename))
             img = skimage.io.imread(fn)
-            img = label_objects(img, nj.parameters.probability_threshold, nj.parameters.min_size)
+            img = label_objects(img, bj.parameters.probability_threshold, bj.parameters.min_size)
             skimage.io.imsave(outfn, img)
 
         # 3. Upload data to Cytomine
-        upload_data(problem_cls, nj, in_imgs, out_path, **nj.flags, monitor_params={
+        upload_data(problem_cls, bj, in_imgs, out_path, **bj.flags, monitor_params={
             "start": 60, "end": 90, "period": 0.1,
             "prefix": "Extracting and uploading polygons from masks"})
         
         # 4. Compute and upload metrics
-        nj.job.update(progress=90, statusComment="Computing and uploading metrics...")
-        upload_metrics(problem_cls, nj, in_imgs, gt_path, out_path, tmp_path, **nj.flags)
+        bj.job.update(progress=90, statusComment="Computing and uploading metrics...")
+        upload_metrics(problem_cls, bj, in_imgs, gt_path, out_path, tmp_path, **bj.flags)
 
         # 5. Pipeline finished
-        nj.job.update(progress=100, status=Job.TERMINATED, status_comment="Finished.")
+        bj.job.update(progress=100, status=Job.TERMINATED, status_comment="Finished.")
 
 
 if __name__ == "__main__":