6151 update feature highlights (#6571)

part of #6151


### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

docs/source/whatsnew.rst

@@ -6,6 +6,7 @@ What's New
 .. toctree::
    :maxdepth: 1
 
+   whatsnew_1_2.md
    whatsnew_1_1.md
    whatsnew_1_0.md
    whatsnew_0_9.md

docs/source/whatsnew_1_1.md

@@ -1,4 +1,4 @@
-# What's new in 1.1 🎉🎉
+# What's new in 1.1
 
 - Digital pathology workflows
 - Experiment management for MONAI bundle

docs/source/whatsnew_1_2.md

@@ -0,0 +1,59 @@
+# What's new in 1.2 🎉🎉
+
+- Auto3DSeg enhancements and benchmarks
+- nnUNet integration
+- TensorRT-optimized networks
+- MetricsReloaded integration
+- Bundle workflow APIs
+- Modular patch inference
+
+## Auto3DSeg enhancements and benchmarks
+Auto3DSeg is an innovative solution for 3D medical image segmentation, leveraging the advancements in MONAI and GPUs for algorithm development and deployment.
+Key improvements in this release include:
+- Several new modules to the training pipelines, such as automated GPU-based hyperparameter scaling, early stopping mechanisms, and dynamic validation frequency.
+- Multi-GPU parallelism has been activated for all GPU-related components including data analysis, model training, and model ensemble, to augment overall performance and capabilities.
+- The algorithms were benchmarked for computational efficiency on the TotalSegmentator dataset, containing over 1,000 CT images.
+- Multi-node training is implemented, reducing model training time significantly.
+
+
+## nnUNet integration
+The integration introduces a new class, `nnUNetV2Runner`, which leverages Python APIs to facilitate model training, validation,
+and ensemble, thereby simplifying the data conversion process for users.
+Benchmarking results from various public datasets confirm that nnUNetV2Runner performs as expected.
+Users are required to prepare a data list and create an `input.yaml` file to install and use the system.
+The framework also allows automatic execution of the entire nnU-Net pipeline, from model training to ensemble,
+with options to specify the number of epochs. Users can access APIs for training, dataset conversion, data preprocessing, and other components.
+Please check out [the tutorials](https://github.com/Project-MONAI/tutorials/tree/main/nnunet) for more details.
+
+## TensorRT-optimized networks
+[NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) is an SDK for high-performance deep learning inference,
+includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.
+It can accelerate the deep learning model forward computation on the NVIDIA GPU.
+In this release, the `trt_export` API to export the TensorRT engine-based TorchScript model has been integrated into the MONAI bundle.
+Users can try to export bundles with it. A few bundles in the MONAI model zoo,
+like the [spleen_ct_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation)
+and [endoscopic_tool_segmentation](https://github.com/Project-MONAI/model-zoo/tree/dev/models/endoscopic_tool_segmentation) bundles,
+have already been exported and benchmarked. For more details about how to export and benchmark a model,
+please go to this [tutorial](https://github.com/Project-MONAI/tutorials/blob/main/acceleration/TensorRT_inference_acceleration.ipynb).
+
+
+## MetricsReloaded integration
+MetricsReloaded - a new recommendation framework for biomedical image analysis validation - is released publicly
+via https://github.com/Project-MONAI/MetricsReloaded. Binary and categorical metrics computing modules are included in this release,
+using MetricsReloaded as the backend. [Example scripts](https://github.com/Project-MONAI/tutorials/tree/main/modules/metrics_reloaded) are made available to demonstrate the usage.
+
+
+## Bundle workflow APIs
+`BundleWorkflow` abstracts the typical workflows (such as training, evaluation, and inference) of a bundle with three main interfaces:
+`initialize`, `run`, and `finalize`, applications use these APIs to execute a bundle.
+It unifies the required properties and optional properties for the workflows, downstream applications
+can invoke the components instead of parsing configs with keys.
+In this release, `ConfigWorkflow` class is also created for JSON and YAML config-based bundle workflows for improved Pythonic usability.
+
+
+## Modular patch inference
+In patch inference, patches are extracted from the image, the inference is run on those patches, and outputs are merged
+to construct the result image corresponding to the input image. Although depending on the task, model, and computational/memory resources,
+the exact implementations of a patch inference may vary, the overall process of splitting, running inference, and merging the results remains the same.
+In this release, we have created a modular design for patch inference, which defines the overall process while abstracting away the specific
+behavior of how to split the image into patches, how to pre and post process each patch, and how to merge the output patches.