diff --git a/README.md b/README.md
index bbbe8ffa..23729c3f 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
# CellSeg3D: self-supervised (and supervised) 3D cell segmentation, primarily for mesoSPIM data!
-[](https://www.napari-hub.org/plugins/napari-cellseg3d)
+[](https://www.napari-hub.org/plugins/napari_cellseg3d)
[](https://pypi.org/project/napari-cellseg3d)
[](https://pepy.tech/project/napari-cellseg3d)
[](https://pepy.tech/project/napari-cellseg3d)
@@ -7,7 +7,7 @@
[](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D)
-
+
**A package for 3D cell segmentation with deep learning, including a napari plugin**: training, inference, and data review. In particular, this project was developed for analysis of confocal and mesoSPIM-acquired (cleared tissue + lightsheet) tissue datasets, but is not limited to this type of data. [Check out our preprint for more information!](https://www.biorxiv.org/content/10.1101/2024.05.17.594691v1)
@@ -38,7 +38,7 @@ To use the plugin, please run:
```
napari
```
-Then go into `Plugins > napari-cellseg3d`, and choose which tool to use.
+Then go into `Plugins > napari_cellseg3d`, and choose which tool to use.
- **Review (label)**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
- **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.