Project-MONAI · MMelQin · Oct 19, 2022 · Oct 17, 2022 · Oct 17, 2022 · Oct 17, 2022
diff --git a/README.md b/README.md
@@ -79,16 +79,15 @@ YouTube Video:
 - [Spleen Organ Segmentation - Jupyter Notebook Tutorial](https://www.youtube.com/watch?v=cqDVxzYt9lY)
 - [Spleen Organ Segmentation - Deep Dive](https://www.youtube.com/watch?v=nivgfD4pwWE)
 
-### [4) Deploying Segmentation app with MONAI Inference Service (MIS)](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/tutorials/04_mis_tutorial.html)
+### [4) Creating a Segmentation app](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/tutorials/03_segmentation_viz_app.html)
 
-### [5) Building and deploying Segmentation app with MONAI Inference Service (MIS)](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/tutorials/05_full_tutorial.html)
-
-### [6) Creating a Segmentation app consuming a MONAI Bundle](https://docs.monai.io/projects/monai-deploy-app-sdk/en/0.4.0/getting_started/tutorials/06_monai_bundle_app.html)
+### [5) Creating a Segmentation app consuming a MONAI Bundle](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/tutorials/06_monai_bundle_app.html)
 
 ### [Examples](https://docs.monai.io/projects/monai-deploy-app-sdk/en/latest/getting_started/examples.html)
 
 <https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps> has example apps that you can see.
 
+- ai_livertumor_seg_app
 - ai_spleen_seg_app
 - ai_unetr_seg_app
 - dicom_series_to_image_app

diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -1,7 +1,7 @@
 Sphinx==4.1.2
 sphinx-autobuild==2021.3.14
 myst-parser==0.15.2
-numpy==1.21.2  # CVE-2021-33430
+numpy>=1.21.2  # CVE-2021-33430
 matplotlib==3.3.4
 ipywidgets==7.6.4
 pandas==1.1.5
@@ -22,11 +22,13 @@ scipy
 scikit-image>=0.17.2
 plotly
 nibabel>=3.2.1
-monai>=0.9.0
+monai>=1.0.0
 torch>=1.10.0
 numpy-stl>=2.12.0
 trimesh>=3.8.11
 pydicom
+PyPDF2>=2.11.1
+highdicom>=0.18.2
 sphinx-autodoc-typehints==1.12.0
 sphinxcontrib-applehelp==1.0.2
 sphinxcontrib-devhelp==1.0.2

diff --git a/docs/source/developing_with_sdk/core_concepts.md b/docs/source/developing_with_sdk/core_concepts.md
@@ -78,7 +78,7 @@ graph TD
 
     click Package "./packaging_app.html" "Go to the document" _self
     click ExecPackage "./executing_packaged_app_locally.html" "Go to the document" _self
-    click DeployPackage "./deploying_to_the_remote_server.html" "Go to the document" _self
+    click DeployPackage "./deploying_and_hosting_map.html" "Go to the document" _self
 ```
 <!-- In the above caption text, it uses Unicode blank characters('⠀⠀') in front of the text to align to center (somehow, it is misaligned little bit) -->
 

diff --git a/docs/source/developing_with_sdk/deploying_and_hosting_map.md b/docs/source/developing_with_sdk/deploying_and_hosting_map.md
@@ -0,0 +1,6 @@
+# Deploying and Hosting MONAI App Package
+
+The MONAI Application Package, MAP, built and packaged using MONAI Deploy App SDK, can be deployed in multiple ways, each with different levels of integration with a hosting platform.
+- A MAP is an OCI compliant container, albeit requiring <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/overview.html"> Nvidia Container Toolkit </a> as it is based on the <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">Nvidia PyTorch Python container image </a>. The hosting platforms, via their own tooling, can inspect the MAP metadata and ensure dependencies are satisfied on launching the MAP container, for example, an MAP can used in a Argo workflow.
+- A MAP runs "natively" on a platform, where the platform specific adaptor or shim embedded in the MAP can understand the MAP and the platform APIs thus manages the life cycle of the application per platform requests. At the onset, the only native execution planned is on the <a href="https://github.com/Project-MONAI/monai-deploy/releases">MONAI Deploy</a>. The support for other platforms is on the horizon.
+- As envisioned for the long term, standardization of deployment package specification will be needed, so that compliant application packages shall work on all compliant platforms. One of the initiatives in this area is the <a href="https://oam.dev/">Open Appication Model</a>, though the App SDK project is not yet actively investigating the integration.
diff --git a/docs/source/developing_with_sdk/deploying_to_the_remote_server.md b/docs/source/developing_with_sdk/deploying_to_the_remote_server.md
diff --git a/docs/source/developing_with_sdk/index.md b/docs/source/developing_with_sdk/index.md
@@ -11,5 +11,5 @@ creating_application_class
 executing_app_locally.ipynb
 packaging_app
 executing_packaged_app_locally
-deploying_to_the_remote_server
+deploying_and_hosting_map
 ```
diff --git a/docs/source/developing_with_sdk/packaging_app.md b/docs/source/developing_with_sdk/packaging_app.md
@@ -6,7 +6,7 @@ In this step, the MONAI Deploy application can now be built into a deployable Do
 
 The MONAI Application Packager (Packager) is a utility for building an application developed with the MONAI Deploy App SDK into a structured MONAI Application Package (**MAP**).
 
-The MAP produced by the Packager is a deployable and reusable docker image that can be launched [locally](./executing_packaged_app_locally) or [remotely](./deploying_to_the_remote_server).
+The MAP produced by the Packager is a deployable and reusable docker image that can be launched [locally](./executing_packaged_app_locally) or [remotely](./deploying_and_hosting_map).
 
 ### Basic Usage of MONAI Application Packager
 
@@ -67,7 +67,7 @@ Building MONAI Application Package...
 Successfully built my_app:latest
 ```
 
-The MAP image `my_app:latest` will be seen in the list of container images on the user's local machine when the command `docker images` is run. The MAP image `my_app:latest` will be able to run [locally](./executing_packaged_app_locally) or [remotely](./deploying_to_the_remote_server).
+The MAP image `my_app:latest` will be seen in the list of container images on the user's local machine when the command `docker images` is run. The MAP image `my_app:latest` will be able to run [locally](./executing_packaged_app_locally) or [remotely](./deploying_and_hosting_map).
 
 ```{note}
 * The current implementation (as of `0.1.0`) of the Packager **ONLY** supports [**CUDA**](https://ngc.nvidia.com/catalog/containers/nvidia:cuda) and [**Pytorch**](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) images from `nvcr.io` as base images for the MAP. There are efforts in progress to add support for smaller images from [dockerhub](https://hub.docker.com/).

diff --git a/docs/source/getting_started/tutorials/04_mis_tutorial.md b/docs/source/getting_started/tutorials/04_mis_tutorial.md
diff --git a/docs/source/getting_started/tutorials/05_full_tutorial.md b/docs/source/getting_started/tutorials/05_full_tutorial.md
diff --git a/docs/source/getting_started/tutorials/index.md b/docs/source/getting_started/tutorials/index.md
@@ -4,10 +4,9 @@
 :glob:
 :maxdepth: 2
 
-01_simple_app
-02_mednist_app
-03_segmentation_app
-04_mis_tutorial
-05_full_tutorial
-06_monai_bundle_app
+simple_app
+mednist_app
+monai_bundle_app
+segmentation_app
+segmentation_clara-viz_app
 ```
diff --git a/...tting_started/tutorials/02_mednist_app.md → .../getting_started/tutorials/mednist_app.md b/...tting_started/tutorials/02_mednist_app.md → .../getting_started/tutorials/mednist_app.md
@@ -1,4 +1,6 @@
-# 2) Creating MedNIST Classifier app
+# Creating MedNIST Classifier App
+
+This tutorial demos the process of packaging up a trained model using MONAI Deploy App SDK into an artifact which can be run as a local program performing inference, a workflow job doing the same, and a Docker containerized workflow execution.
 
 ## Setup
 

diff --git a/..._started/tutorials/06_monai_bundle_app.md → ...ing_started/tutorials/monai_bundle_app.md b/..._started/tutorials/06_monai_bundle_app.md → ...ing_started/tutorials/monai_bundle_app.md
@@ -1,4 +1,6 @@
-# 6) Creating a Segmentation app consuming a MONAI Bundle
+# Creating a Segmentation App Consuming a MONAI Bundle
+
+This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI and packaged in the [MONAI Bundle](https://docs.monai.io/en/latest/bundle_intro.html) format.
 
 ## Setup
 
@@ -39,20 +41,20 @@ git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-
 cd monai-deploy-app-sdk
 
 # Install monai-deploy-app-sdk package
-pip install monai-deploy-app-sdk
+pip install --upgrade monai-deploy-app-sdk
 
 # Download/Extract ai_spleen_bundle_data zip file from https://drive.google.com/file/d/1cJq0iQh_yzYIxVElSlVa141aEmHZADJh/view?usp=sharing
 
-# Download ai_spleen_bundle_data.zip
+# Download the zip file containing both the model and test data
 pip install gdown
-gdown https://drive.google.com/uc?id=1cJq0iQh_yzYIxVElSlVa141aEmHZADJh
+gdown https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ
 
-# After downloading ai_spleen_bundle_data.zip from the web browser or using gdown,
-unzip -o ai_spleen_bundle_data.zip
+# After downloading it using gdown, unzip the zip file saved by gdown
+unzip -o ai_spleen_seg_bundle_data.zip
 
 # Install necessary packages from the app; note that numpy-stl and trimesh are only
 # needed if the application uses the STL Conversion Operator
-pip install monai pydicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh
+pip install monai torch pydicom highdicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh
 
 # Local execution of the app directly or using MONAI Deploy CLI
 python examples/apps/ai_spleen_seg_app/app.py -i dcm/ -o output -m model.ts

diff --git a/..._started/tutorials/03_segmentation_app.md → ...ing_started/tutorials/segmentation_app.md b/..._started/tutorials/03_segmentation_app.md → ...ing_started/tutorials/segmentation_app.md
@@ -1,4 +1,6 @@
-# 3) Creating a Segmentation app
+# Creating a Segmentation App
+
+This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI. Please note that the example code used in the Jupyter Notebook is based on an earlier version of the segmentation application, i.e., not using MONAI Bundle Inference Operator, and the code is not necessarily the same as the latest source code on Github.
 
 ## Setup
 
@@ -15,7 +17,7 @@ jupyter-lab
 ```
 
 ## Executing from Jupyter Notebook
-Please note that the example code used in the Jupyter Notebook is based on an earlier version of the segmentation application, hence not the same as the latest source code on Github, e.g. not using MONAI Bundle inference operator.
+
 ```{toctree}
 :maxdepth: 4
 
@@ -51,20 +53,20 @@ git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-
 cd monai-deploy-app-sdk
 
 # Install monai-deploy-app-sdk package
-pip install monai-deploy-app-sdk
+pip install --upgrade monai-deploy-app-sdk
 
 # Download/Extract ai_spleen_bundle_data zip file from https://drive.google.com/file/d/1cJq0iQh_yzYIxVElSlVa141aEmHZADJh/view?usp=sharing
 
-# Download ai_spleen_bundle_data.zip
+# Download the zip file containing both the model and test data
 pip install gdown
-gdown https://drive.google.com/uc?id=1cJq0iQh_yzYIxVElSlVa141aEmHZADJh
+gdown https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ
 
-# After downloading ai_spleen_bundle_data.zip from the web browser or using gdown,
-unzip -o ai_spleen_bundle_data.zip
+# After downloading it using gdown, unzip the zip file saved by gdown
+unzip -o ai_spleen_seg_bundle_data.zip
 
 # Install necessary packages from the app; note that numpy-stl and trimesh are only
 # needed if the application uses the STL Conversion Operator
-pip install monai pydicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh
+pip install monai pydicom highdicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh
 
 # Local execution of the app
 python examples/apps/ai_spleen_seg_app/app.py -i dcm/ -o output -m model.ts

diff --git a/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md b/docs/source/getting_started/tutorials/segmentation_clara-viz_app.md
@@ -0,0 +1,33 @@
+# Creating a Segmentation App Including Including Visualization with Clara-Viz
+
+This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI, and visualize the segmentation and input images with Clara Viz integration.
+
+## Setup
+
+```bash
+# Create a virtual environment with Python 3.7.
+# Skip if you are already in a virtual environment.
+# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
+#  See https://github.com/jupyterlab/jupyterlab/pull/11740)
+conda create -n monai python=3.7 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
+conda activate monai
+
+# Launch JupyterLab if you want to work on Jupyter Notebook
+jupyter-lab
+```
+
+## Executing from Jupyter Notebook
+
+```{toctree}
+:maxdepth: 4
+
+../../notebooks/tutorials/03_segmentation_viz_app.ipynb
+```
+
+```{raw} html
+<p style="text-align: center;">
+    <a class="sphinx-bs btn text-wrap btn-outline-primary col-md-6 reference external" href="../../_static/notebooks/tutorials/03_segmentation_viz_app.ipynb">
+        <span>Download 03_segmentation_viz_app.ipynb</span>
+    </a>
+</p>
+```
diff --git a/...etting_started/tutorials/01_simple_app.md → ...e/getting_started/tutorials/simple_app.md b/...etting_started/tutorials/01_simple_app.md → ...e/getting_started/tutorials/simple_app.md
@@ -1,4 +1,6 @@
-# 1) Creating a simple image processing app
+# Creating a simple image processing app
+
+This tutorial shows how to develop a simple image processing application can be created with MONAI Deploy App SDK.
 
 ## Setup
 

diff --git a/docs/source/introduction/overview.md b/docs/source/introduction/overview.md
@@ -9,3 +9,17 @@ It contains the following elements:
 - A mechanism to locally run a MAP via App Runner
 - A set of sample applications
 - API documentation
+
+To further accelerate the development of medical imaging AI inference application with DICOM imaging network integration, a set of domain specific functionalities are provided by this Application SDK,
+- generic application classes to automate the inference with MONAI Bundle
+- DICOM study parsing and selection classes, as well as DICOM instance to volume image conversion
+- DICOM instance writers to encapsulate AI inference results in these DICOM OID,
+  - DICOM Segmentation
+  - DICOM Basic Text Structured Report
+  - DICOM Encapsulated PDF
+  - more are coming
+
+:::{note}
+- The software and the imaging AI results generated are for research use only, and per applicable laws and regulations, are not for clinical use.
+- The App SDK, and specifically the DICOM instance writers, rely upon the underlying operating system to provide accurate and consistent time, though have no direct control over how the OS performs time synchronization, e.g. NTP on Ubuntu. For creation of DICOM or HL7 objects that use time, the built-in classes do, and it would also be incumbent on the developer to appropriately use the available system time service and reflect the correct time in those objects properly. Additionally, the DICOM instance generated by the App SDK built-in classes set the <a href="https://dicom.nema.org/medical/dicom/2020b/output/chtml/part03/sect_C.12.5.html">Timezone Offset From UTC</a> with the underlying operating system's timezone setting.
+:::
diff --git a/docs/source/introduction/roadmap.md b/docs/source/introduction/roadmap.md
@@ -1,5 +1,5 @@
 # Roadmap
 
-The first version of the MONAI Deploy App SDK offers a core framework to build & package healthcare AI apps so that they can be deployed to a production environment
+The first versions of the MONAI Deploy App SDK offer a core framework to build & package healthcare AI apps so that they can be deployed to a production environment.
 
-We are currently in the process of creating a roadmap for the product based on the community’s input.
+We are currently in the process of refining the roadmap for the product based on the community’s input, though it is clear that on the roadmap are more built-in DICOM parsing and DICOM OID generation capabilities to better support <a href="https://www.ihe.net/uploadedFiles/Documents/Radiology/IHE_RAD_Suppl_AIR_Rev1-2_TI_2022-07-06.pdf">IHE AIR profiles</a>, as well as serving model network in a separate process.
diff --git a/docs/source/release_notes/index.md b/docs/source/release_notes/index.md
@@ -4,6 +4,13 @@
 :hidden:
 :maxdepth: 2
 
+```
+## Version 0.5
+
+```{toctree}
+:maxdepth: 1
+
+v0.5.0
 ```
 ## Version 0.4