Update vista3d tutorial readme (#1859)

Fixes #1856.

### Description
A few sentences describing the changes proposed in this pull request.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

---------

Signed-off-by: heyufan1995 <heyufan1995@gmail.com>
Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

Author: heyufan1995

vista_3d/README.md

@@ -1,4 +1,21 @@
+# MONAI **V**ersatile **I**maging **S**egmen**T**ation and **A**nnotation
+[[`Paper`](https://arxiv.org/pdf/2406.05285)] [[`Demo`](https://build.nvidia.com/nvidia/vista-3d)] [[`Checkpoint`](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_vista3d.pt)]
+## Overview
 
+The **VISTA3D** is a foundation model trained systematically on 11,454 volumes encompassing 127 types of human anatomical structures and various lesions. It provides accurate out-of-the-box segmentation that matches state-of-the-art supervised models which are trained on each dataset. The model also achieves state-of-the-art zero-shot interactive segmentation in 3D, representing a promising step toward developing a versatile medical image foundation model.
+
+The tutorial demonstrates how to finetune the VISTA3D model on user data, where we use the MSD Task09 Spleen as the example.
+
+In Summary the tutorial covers the following:
+- Creation of datasets and data transforms for training and validation
+- Create and VISTA3D model and load the pretrained checkpoint
+- Implementation of the finetuning loop
+- Mixed precision training with GradScaler
+- Visualization of training loss and validation accuracy
+- Inference on a single validation image
+- Visualization of input image, ground truth, and model prediction
+
+For more advanced use, please refer to [VISTA3D research codebase](https://github.com/Project-MONAI/VISTA/tree/main/vista3d) and [VISTA3D bundle](https://github.com/Project-MONAI/model-zoo/tree/dev/models/vista3d)
 ## License
 
 The codebase is under Apache 2.0 Licence. The model weight is released under [NVIDIA OneWay Noncommercial License](./NVIDIA%20OneWay%20Noncommercial%20License.txt).