diff --git a/doc/apg.md b/doc/apg.md
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@@ -3,9 +3,10 @@
 `micro_sam` supports three different modes for instance segmentation:
 - Automatic Mask Generation (AMG) covers the image with a grid of points. These points are used as prompts and the resulting masks are merged via non-maximum suppression (NMS) to obtain the instance segmentation. This method has been introduced by the original SAM publication.
 - Automatic Instance Segmentation (AIS) uses an additional segmentation decoder, which we introduced in the `micro_sam` publication. This decoder predicts foreground probabilities as well as the normalized distances to cell centroids and boundaries. These predictions are used as input to a waterhsed to obtain the instances.
-- Autmatic Prompt Generation (APG) is an instance segmentation approach that we introduced in [a new paper](https://openreview.net/forum?id=xFO3DFZN45). It derives point prompts from the segmentation decoder (see AIS) and merges the resulting masks via NMS.
+- Automatic Prompt Generation (APG) is an instance segmentation approach that we introduced in [a new paper](https://openreview.net/forum?id=xFO3DFZN45). It derives point prompts from the segmentation decoder (see AIS) and merges the resulting masks via NMS.
 
-In our experiments, APG yields the best overall instance segmentation results (compared to AMG and AIS) and is competitive with CellPose-SAM, the state-of-the-art model for cell instance segmentation.
+In our experiments, APG yields the best overall instance segmentation results (compared to AMG and AIS) and is competitive with [CellPose-SAM](https://doi.org/10.1101/2025.04.28.651001
+), the state-of-the-art model for cell instance segmentation.
 
 The segmentation mode can be selected with the argument `mode` or `segmentation_mode` in the [CLI](#using-the-command-line-interface-cli) and [python functionality](https://computational-cell-analytics.github.io/micro-sam/micro_sam/automatic_segmentation.html). For details on how to use the different automatic segmentation modes check out the [automatic segmentation
 notebook](https://github.com/computational-cell-analytics/micro-sam/blob/master/notebooks/automatic_segmentation.ipynb). The code for the experiments comparing the different segmentation modes (from [the new paper](https://openreview.net/forum?id=xFO3DFZN45)) can be found [here](https://github.com/computational-cell-analytics/micro-sam/tree/master/scripts/apg_experiments).