Starting updates to docs

docs/source/bundle_intro.rst

@@ -3,6 +3,11 @@
 Bundle
 ======
 
+MONAI Bundles are a specification and file structure based way of distributing trained MONAI models with associated
+metadata, code, documentation, and other resources. These are meant to make it easier for you to distribute your model
+in a format that explains what the model is for, how to use it, how to reproduce the science you've done with it, and
+use it in other applications such as Label and Deploy.
+
 .. toctree::
    :maxdepth: 1
 
@@ -13,3 +18,26 @@ Bundle
 Detailed bundle examples and get started tutorial: https://github.com/Project-MONAI/tutorials/tree/main/bundle
 
 A collection of medical imaging models in the MONAI Bundle format: https://github.com/Project-MONAI/model-zoo
+
+
+Bundle vs. MAPs
+---------------
+
+Bundles differ from MONAI Application Packages (MAPs) in that they focus on description, code definition, application,
+usage. MAPs focus on deployment, containerisation, integration into existing clinical systems, and other application
+areas relating to putting models into use.
+
+.. image:: ../images/MONAI_clouds.png
+    :alt: Bundle and MAP Concepts
+    :align: center
+
+As a user, bundles are networks and "programs" you would use directly for training, inference, reproducing results,
+and other tasks. Bundles can be integrated into MONAI Label apps to perform segmentation tasks through user interfaces,
+or into MAPs for deployment. They can be integrated into other container environments but this isn't their focus. A
+bundle in general is a more lightweight concept with less infrastructure
+
+For all applications relating to containerisation, portability, and deployment, MAPs are what you're looking for. A MAP
+is the contained environment for running an inference application directly or within an orchestration system. A bundle
+alone doesn't have the structure suitable for this use, a MAP must be provided which uses a bundle as the inference object.
+MAPs are also meant for inference only unlike bundles which should include training scripts. DICOM access is emphasised in
+MAPs since they are meant for clinical deployment and so must interface with clinical databases.

docs/source/index.rst

@@ -20,6 +20,10 @@ Its ambitions are:
 - creating state-of-the-art, end-to-end training workflows for healthcare imaging;
 - providing researchers with an optimized and standardized way to create and evaluate deep learning models.
 
+.. image:: ../images/MONAI_arch.png
+    :alt: MONAI Architecture
+    :align: center
+
 Features
 --------
 

docs/source/mb_specification.rst

@@ -153,7 +153,7 @@ An example JSON metadata file:
                   "dtype": "float32",
                   "value_range": [0, 1],
                   "is_patch_data": false,
-                  "channel_def": {0: "image"}
+                  "channel_def": {"0": "image"}
               }
           },
           "outputs":{
@@ -165,7 +165,7 @@ An example JSON metadata file:
                   "dtype": "float32",
                   "value_range": [],
                   "is_patch_data": false,
-                  "channel_def": {0: "background", 1: "spleen"}
+                  "channel_def": {"0": "background", "1": "spleen"}
               }
           }
       }