diff --git a/README.md b/README.md
index b76f5276..42b26d94 100644
--- a/README.md
+++ b/README.md
@@ -21,15 +21,14 @@ Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Ce
 
 ### CITATION
 
-If you use the original built-in models (`cyto` or `nuclei`), please cite the Cellpose 1.0 [paper](https://t.co/kBMXmPp3Yn?amp=1):  
-Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). <em> Cellpose: a generalist algorithm for cellular segmentation </em>. Nature methods, 18(1), 100-106.
+**If you use Cellpose 1 or 2, please cite the Cellpose 1.0 [paper](https://t.co/kBMXmPp3Yn?amp=1):**  
+Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. <em>Nature methods, 18</em>(1), 100-106.
+([bibtex link](https://scholar.googleusercontent.com/scholar.bib?q=info:rmoKTp0cEiYJ:scholar.google.com/&output=citation&scisdr=CgXHFLYtEMb9qOfkmrg:AAGBfm0AAAAAY2jigrhA_p9qteLfyKDZlh96dZdapgkX&scisig=AAGBfm0AAAAAY2jigv55oXhgKwSArS2sr_fxBh--42gU&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1))
 
-If you use the new human-in-the-loop training or the new models, please cite the Cellpose 2.0 [paper](https://www.biorxiv.org/content/10.1101/2022.04.01.486764v1):  
-Stringer, C., & Pachitariu, M. (2022). <em> Cellpose 2.0: how to train your own model </em>. bioRxiv.
+**If you use the new human-in-the-loop training or use the new cyto2, livecell, or tissuenet models, please also cite the Cellpose 2.0 [paper](https://www.nature.com/articles/s41592-022-01663-4):**
+Pachitariu, M. & Stringer, C. (2022). Cellpose 2.0: how to train your own model. <em>Nature methods</em>.
 
-:triangular_flag_on_post: the new `tissuenet`, `TN1`, `TN2`, `TN3`, `livecell`, `LC1`, `LC2`, `LC3` and `LC4` were trained using data under a **CC-BY-NC** license, so these models CANNOT be used for commercial applications.
-
-If you want to improve Cellpose for yourself and for everyone else, please consider contributing manual segmentations for a few of your images via the built-in GUI interface (see instructions below).
+:triangular_flag_on_post: the new tissuenet and livecell models (`tissuenet`, `TN1`, `TN2`, `TN3`, `livecell`, `LC1`, `LC2`, `LC3` and `LC4`)  were trained using data under a **CC-BY-NC** license, so these models are **non-commercial use only**.
 
 ### :star2: UPDATE v2.0 (April 2022) :star2:
 
diff --git a/cellpose/dynamics.py b/cellpose/dynamics.py
index a90b9f34..58f206c3 100644
--- a/cellpose/dynamics.py
+++ b/cellpose/dynamics.py
@@ -320,7 +320,7 @@ def labels_to_flows(labels, files=None, use_gpu=False, device=None, redo_flows=F
         if files is not None:
             for flow, file in zip(flows, files):
                 file_name = os.path.splitext(file)[0]
-                tifffile.imsave(file_name+'_flows.tif', flow)
+                tifffile.imwrite(file_name+'_flows.tif', flow)
     else:
         dynamics_logger.info('flows precomputed')
         flows = [labels[n].astype(np.float32) for n in range(nimg)]
diff --git a/cellpose/io.py b/cellpose/io.py
index 280f3724..49900924 100644
--- a/cellpose/io.py
+++ b/cellpose/io.py
@@ -144,7 +144,7 @@ def add_model(filename):
 def imsave(filename, arr):
     ext = os.path.splitext(filename)[-1].lower()
     if ext== '.tif' or ext=='.tiff':
-        tifffile.imsave(filename, arr)
+        tifffile.imwrite(filename, arr)
     else:
         if len(arr.shape)>2:
             arr = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)