Update README.md

Signed-off-by: myron <[email protected]>

Author: myron

auto3dseg/tasks/kits23/README.md

@@ -1,30 +1,57 @@
 
 # KiTS23 
 
-The KiTS dataset is from MICCAI 2023 challenge **[The 2023 Kidney and Kidney Tumor Segmentation Challenge (KiTS23)](https://kits-challenge.org/kits23/)**. The solution described here won the 1st place in the KiTS challenge [(NVAUTO team)](https://kits-challenge.org/kits23/#kits23-official-results):
+<img align="right" src="./kits23_example.png" width=30%>
+
+This tutorial shows how to use Auto3DSeg with KiTS 2023 dataset from the MICCAI 2023 challenge **[The 2023 Kidney and Kidney Tumor Segmentation Challenge (KiTS23)](https://kits-challenge.org/kits23/)**. 
+The example is based on the 1st place solution in the KiTS challenge [(NVAUTO team)](https://kits-challenge.org/kits23/#kits23-official-results):
 
 Andriy Myronenko, Dong Yang, Yufan He and Daguang Xu: "Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge". In MICCAI (2023). [arXiv](https://arxiv.org/abs/2310.04110)
 
-![kits23_example](./kits23_example.png)
 
 ## Task overview
 
-The task is to segment kidneys, tumors and cysts from 3D CTs.  The ground truth labels are provided for 489 cases with resolutions between 0.39x0.39x0.5 and 1x1x5 mm. 
-
+The task is to segment kidneys, tumors and cysts from 3D CTs. The dataset contains 489 cases with resolutions rangingn between 0.39x0.39x0.5 and 1x1x5 mm. 
+Please download the KiTS23 [dataset](https://kits-challenge.org/kits23/#) and place it in the "/data/kits23" folder to follow this tutorial. 
 
 ## Auto3DSeg
 
-The KiTS tutorial is only supported for **SegResNet** algo, Auto3DSeg runs a full workflow including data analysis, and multi-fold training. Please download the dataset into /data/kits23 folder first.
+With Auto3DSeg most segmentation parameters are automatically determined.  In this tutorial, we start from a basic automated example, then show how different options can be adjusted if necessary.  We use only the  **SegResNet** algo here for simplicity, which is a training recepie based on the [segresnet] (https://docs.monai.io/en/latest/networks.html#segresnetds). 
 
 
-### Running based on the input config
+### Running based on the input config (one-liner)
 
 The Auto3DSeg can be run using a config **input.yaml**
 
 ```bash
 python -m monai.apps.auto3dseg AutoRunner run --input=./input.yaml --algos=segresnet
 ```
 
+This one line of code will run the full training workflow, including data analysis, multi-fold training, ensembling. The system will adjust parameters based on the data and your available GPU (or multi-GPU) hardware configuration. 
+Here we explicitely specified to use only segresnet algo, for other possible parameters of the AutoRunner please see the [monai docs](https://github.com/Project-MONAI/MONAI/blob/main/monai/apps/auto3dseg/auto_runner.py). 
+
+The [input.yaml](./input.yaml) describes the dataset (KiTS23) task, and must include at least 3 mandatory fields: mondality (CT), dataset location (here it's /data/kits23) and the dataset manifest json file [kits23_folds.json](./kits23_folds.json).
+Other parameters can optionally be also added to the input.yaml config.   For KiTS23 dataset specifically, we include the "class_names" key that show the label grouping for the 3 output classes that KiTS23 challenge asks (which is something specific for the KiTS task)
+
+### Running from the code
+
+If you prefer to run the same thing from code (which will allow more customizations), once can create a python file "example.py" and simply run it as 
+```bash
+python example.py
+```
+```python
+# example.py file
+
+from monai.apps.auto3dseg import AutoRunner
+
+def main():
+    runner = AutoRunner(input='./input.yaml', algos = 'segresnet')
+    runner.run()
+
+if __name__ == '__main__':
+  main()
+```
+
 ## Validation performance: NVIDIA DGX-1 (8x V100 32G)
 
 The validation results can be obtained by running the training script with MONAI 1.3.0 on NVIDIA DGX-1 with (8x V100 32GB) GPUs. The results below are in terms of average dice.