added download link for Docker container

Author: dnschouten

README.md

@@ -19,7 +19,7 @@ The input for Pythostitcher consists of either two or four high resolution histo
 After preprocessing the input images, Pythostitcher will perform an automated stitch edge detection and compute a rough initial alignment on heavily downsampled versions of the input images. This initial alignment is then iteratively refined using images with an increasingly finer resolution. This refinement is performed by a genetic algorithm, which aims to minimize the average distance between the stitch edges of adjacent fragments. One of the strengths of Pythostitcher is that the optimal alignment between fragments can be scaled linearly for finer resolutions. Hence, when a satisfactory alignment is achieved on a lower resolution, this alignment can be scaled up linearly to compute the full resolution stitched result. This greatly improves computational overhead, since the full resolution images can be up to ~100k pixels in height/width, making any direct image processing infeasible on a regular clinical workstation.
 
 ## Usage instructions
-It is highly recommended to run PythoStitcher as a Docker container, since PythoStitcher uses [PyVips](https://github.com/libvips/pyvips) and the backend from [ASAP](https://github.com/computationalpathologygroup/ASAP), which both can not be readily pip installed. The Docker container comes prepackaged with these libraries, as well as with model weights of the involved CNNs, and should run out-of-the-box. Please contact us to obtain the Docker container.
+It is highly recommended to run PythoStitcher as a Docker container, since PythoStitcher uses [PyVips](https://github.com/libvips/pyvips) and the backend from [ASAP](https://github.com/computationalpathologygroup/ASAP), which both can not be readily pip installed. The Docker container comes prepackaged with these libraries, as well as with model weights of the involved CNNs, and should run out-of-the-box. You can download the container [here](https://filesender.surf.nl/?s=download&token=708ac00a-d2ce-4576-acf9-366c940de051).
 
 You can try out Pythostitcher yourself on your data or on the sample data available from <a href="https://zenodo.org/record/7636102"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7636102.svg" alt="DOI"></a>. The sample data includes two prostatectomy cases, one case with four fragments and one case with two fragments. After downloading the data, save it somewhere on your disk and provide the path to this directory as an argument to the PythoStitcher container. If you want to enforce the location of each fragment in the final reconstruction, be sure to include a force_config.txt file in each patient directory. See the example_force_config.txt file on how to format this.