Update README.md

README.md

@@ -39,10 +39,10 @@ denoising             |  deblurring                  |  upsampling
 **If you use Cellpose 1, 2 or 3, 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.
 
-**If you use the new human-in-the-loop training, please also cite the Cellpose 2.0 [paper](https://www.nature.com/articles/s41592-022-01663-4):**  
+**If you use the human-in-the-loop training, 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>, 1-8.
 
-**If you use the restoration models, please also cite the Cellpose3 [paper](https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1):**
+**If you use the new image restoration models or cyto3, please also cite the Cellpose3 [paper](https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1):**
 Stringer, C. & Pachitariu, M. (2024). Cellpose3: one-click image restoration for improved segmentation. <em>bioRxiv</em>.
 
 :triangular_flag_on_post: All models in Cellpose, except `yeast_BF_cp3`, `yeast_PhC_cp3`, and `deepbacs_cp3`, are trained on some amount of data that is **CC-BY-NC**. The Cellpose annotated dataset is also CC-BY-NC.