add link to readme file (#61)

Co-authored-by: Lorenz Lamm <[email protected]>

README.md

@@ -7,7 +7,10 @@
 [![codecov](https://codecov.io/gh/teamtomo/membrain-seg/branch/main/graph/badge.svg)](https://codecov.io/gh/teamtomo/membrain-seg)
 
 
-Membrain-Seg is a Python project developed by [teamtomo](https://github.com/teamtomo) for membrane segmentation in 3D for cryo-electron tomography (cryo-ET). This tool aims to provide researchers with an efficient and reliable method for segmenting membranes in 3D microscopic images. Membrain-Seg is currently under early development, so we may make breaking changes between releases.
+Membrain-Seg<sup>1</sup> is a Python project developed by [teamtomo](https://github.com/teamtomo) for membrane segmentation in 3D for cryo-electron tomography (cryo-ET). This tool aims to provide researchers with an efficient and reliable method for segmenting membranes in 3D microscopic images. Membrain-Seg is currently under early development, so we may make breaking changes between releases.
+
+**Publication**: Membrain-seg's current functionalities are described on more detail in our [preprint](https://www.biorxiv.org/content/10.1101/2024.01.05.574336v1).
+
 
 <p align="center" width="100%">
     <img width="100%" src="https://user-images.githubusercontent.com/34575029/248259282-ee622267-77fa-4c88-ad38-ad0cfd76b810.png">
@@ -18,7 +21,7 @@ Membrain-Seg is currently under early development, so we may make breaking chang
 
 # Overview
 MemBrain-seg is a practical tool for membrane segmentation in cryo-electron tomograms. It's built on the U-Net architecture and makes use of a pre-trained model for efficient performance.
-The U-Net architecture and training parameters are largely inspired by nnUNet<sup>1</sup>.
+The U-Net architecture and training parameters are largely inspired by nnUNet<sup>2</sup>.
 
 
 Our current best model is available for download [here](https://drive.google.com/file/d/1tSQIz_UCsQZNfyHg0RxD-4meFgolszo8/view?usp=sharing). Please let us know how it works for you.
@@ -38,7 +41,9 @@ Explore MemBrain-seg, use it for your needs, and let us know how it works for yo
 Preliminary [documentation](https://github.com/teamtomo/membrain-seg/blob/main/docs/index.md) is available, but far from perfect. Please let us know if you encounter any issues, and we are more than happy to help (and get feedback what does not work yet).
 
 ```
-[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H., 2021. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18, 203-211. https://doi.org/10.1038/s41592-020-01008-z
+[1] Lamm, L., Zufferey, S., Righetto, R.D., Wietrzynski, W., Yamauchi, K.A., Burt, A., Liu, Y., Zhang, H., Martinez-Sanchez, A., Ziegler, S., Isensee, F., Schnabel, J.A., Engel, B.D., and Peng, T, 2024. MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography. bioRxiv, https://doi.org/10.1101/2024.01.05.574336
+
+[2] Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H., 2021. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18, 203-211. https://doi.org/10.1038/s41592-020-01008-z
 ```
 
 # Installation