Update documentation

Author: constantinpape

doc/start_page.md

@@ -58,31 +58,69 @@ to use them.
 > Note: If you have issues with the CUDA version then install a PyTorch that matches your nvidia drivers. See [pytorch.org](https://pytorch.org/) for details.
 
 
+## Segmentation Models
+
+We currently offer 7 different models for segmenting synaptic structures:
+- `vesicles_3d` to segment vesicles in (room-temperature) electron tomograms.
+- `vesicles_2d` to segment vesicles in two-dimensional electron migrographs.
+- `vesicles_cryo` to segment vesicles in cryogenic electron tomograms.
+- `active_zone` to sement active zones in electron tomograms.
+- `compartments` to segment synaptic compartments in electron tomograms.
+- `mitochondria` to segmenta mitochondria in electron tomograms.
+- `ribbon` to segment structures of the active zones in ribbon synapses (ribbon, presynaptic density and active zone membrane) in electron tomograms.
+
+
 ## Napari Plugin
 
-**The rest of the documentation will be updated in the next days!**
+**The napari plugin will be documented in the next few days!**
 
 
 ## Command Line Functionality
 
-- segmentation cli
-- export to imod
-    - vesicles / spheres
-    - objects
+SynapseNet provides a command line interface to segment synaptic structures in mrc files (or other image formats), and to export segmentation results to IMOD.
+
+**Segmentation CLI:** The command `synapse_net.run_segmentation` enables segmentation with all of our [supported models](#segmentation-models). For example, you can call it like this:
+```bash
+synapse_net.run_segmentation -i /path/to/folder-with-mrc -o /path/to/save-segmentation -m vesicles_3d
+```
+to segment the synaptic vesicles in all tomograms that are stored as mrc files in the folder `/path/to/folder-with-mrc`. The segmentations will be stored as tif files in the (new) folder `/path/to/save-segmentation`.
+You can select a different segmentation model by changing the name after `-m`. For example use `-m mitochondria` to segment mitochondria or `-m vesicles_2d` to segment vesicles in 2D images.
+The command offers several other arguments to change the segmentation logic; you can run `synapse_net.run_segmentation -h` for an explanation of these arguments.
+
+**IMOD Export:** We offer two commands to export segmentation results to mod files that can be opened with [3dmod](https://bio3d.colorado.edu/imod/doc/3dmodguide.html), which is part of the [IMOD](https://bio3d.colorado.edu/imod/) software suite:
+- `synapse_net.export_to_imod_points` to export a vesicle segmentation to a point model; i.e., representing each vesicle as a sphere.
+- `synapse_net.export_to_imod_objects` to export an arbitrary segmentation to a closed contour model.
+
+For example, you can run
+```bash
+synapse_net.export_to_imod_points -i /path/to/folder-with-mrc -s /path/to/save-segmentation -o /path/to/save-modfiles
+```
+to export the segmentations saved in `/path/to/save-segmentation` to point models that will be saved in `/path/to/save-modfiles`.
+
+For more options supported by the IMOD exports, please run `synapse_net.export_to_imod_points -h` or `synapse_net.export_to_imod_objects -h`.
+
+> Note: to use these commands you have to install IMOD.
 
 
 ## Python Library
 
-- segmentation functions
-- distance and morphology measurements
-- imod
+Using the `synapse_net` python library offers the most flexibility for using the SynapseNet functionality.
+We offer different functionality for segmenting and analyzing synapses in electron microscopy:
+- `synpase_net.inference` for segmenting synaptic structures with [our models](segmentation-models).
+- `synapse_net.distance_measurements` for measuring distances between segmented objects.
+- `synapse_net.imod` for importing and exporting segmentations from / to IMOD.
+- `synapse_net.training` for training U-Nets for synaptic structure segmentation, either via [domain adaptation](#domain-adaptation) or [using data with annotations](network-training).
+
+Please refer to the library documentation below for a full overview of our library's functionality.
 
 ### Domain Adaptation
 
-- explain domain adaptation
-- link to the example script
+We provide functionality for domain adaptation. It implements a special form of neural network training that can improve segmentation for data from a different condition (e.g. different sample preparation, electron microscopy technique or different specimen), **without requiring additional annotated structures**.
+Domain adaptation is implemented in `synapse_net.training.domain_adaptation`. You can find an example script that shows how to use it [here](https://github.com/computational-cell-analytics/synapse-net/blob/main/examples/domain_adaptation.py).
+
+> Note: Domain adaptation only works if the initial model you adapt already finds some of the structures in the data from a new condition. If it does not work you will have to train a network on annotated data.
 
 ### Network Training
 
-- explain / diff to domain adaptation
-- link to the example script
+We also provide functionality for 'regular' neural network training. In this case, you have to provide data **and** manual annotations for the structure(s) you want to segment.
+This functionality is implemented in `synapse_net.training.supervised_training`. You can find an example script that shows how to use it [here](https://github.com/computational-cell-analytics/synapse-net/blob/main/examples/network_training.py).