Project-MONAI
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@@ -130,6 +130,12 @@ This tutorial demonstrates the use of MONAI for training of registration and seg
 #### [Deepgrow](./deepgrow)
 The example show how to train/validate a 2D/3D deepgrow model.  It also demonstrates running an inference for trained deepgrow models.
 
+**DeepEdit**
+#### [DeepEdit](./deepedit/ignite)
+This example shows how to train/test a DeepEdit model. In this tutorial there is a Notebook that shows how to run
+inference on a pretrained DeepEdit model.
+
+
 **deployment**
 #### [BentoML](./deployment/bentoml)
 This is a simple example of training and deploying a MONAI network with [BentoML](https://www.bentoml.ai/) as a web server, either locally using the BentoML respository or as a containerized service.
 
@@ -0,0 +1,88 @@
+# DeepEdit
+
+
+DeepEdit is a method that combines an automatic and a semi-automatic approach for 3D medical images into a
+single deep learning-based model. DeepEdit has three working modes: first, it can be used in click-free
+inference mode (similar to a regular segmentation network), providing fully-automatic segmentation predictions which
+can be used as a form of initialisation; second, it allows users to provide clicks to initialise and guide
+a semi-automatic segmentation model; lastly, given an initial segmentation, DeepEdit can be used to refine
+and improve the initial prediction by providing editing clicks. DeepEdit training process is similar to the algorithm
+proposed by **_Sakinis et al._** [DeepGrow](../../deepgrow/ignite) - Gaussian-smoothed clicks for all labels and background
+are generated and added as input to the backbone CNN, but removes the minimum-click limitation of DeepGrow.
+Contrary to DeepGrow, DeepEdit model allows the prediction of an automatic segmentation-based
+initialisation without user-provided clicks, which can then be further edited by providing clicks. Additionally,
+DeepEdit can also be used for multi-label segmentation problems, allowing the user to generate/segment
+all labels simultaneously instead of one label at a time.
+
+This tutorial contains an example to train a DeepEdit model and a notebook to run inference
+over a pre-trained model. The train file reads images and labels folders (imagesTr, labelsTr) as they come in the
+[Medical Segmentation Decathlon](https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar).
+
+More information about the transforms used in DeepEdit are also in the Notebook.
+
+**_Important note:_**
+
+This tutorial is intended to show how to train and test a DeepEdit model in MONAI Core library. Users may also find interesting
+DeepEdit model working in [MONAI Label](https://github.com/Project-MONAI/MONAILabel/tree/main/sample-apps/radiology#deepedit)
+platform. There you can find how DeepEdit works along with 3D Slicer and/or OHIF.
+
+<p align="center">
+  <img src="../../figures/general_schema_deepedit.png" alt="deepedit scheme">
+</p>
+
+**_Sakinis et al.,_** Interactive segmentation of medical images through
+fully convolutional neural networks. (2019) https://arxiv.org/abs/1903.08205
+
+### 1. Data
+
+A DeepEdit model could be trained on any 3D medical image dataset.
+
+For this tutorial we used the public available dataset (Task09_Spleen) that can be downloaded from [Medical Segmentation Decathlon](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2)
+
+### 2. Questions and bugs
+
+- For questions relating to the use of MONAI, please us our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI.
+
+- For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues).
+
+- For bugs relating to the running of a tutorial, please create an issue in [this repository](https://github.com/Project-MONAI/Tutorials/issues).
+
+### 3. List of notebooks and examples
+
+#### Prepare Your Data
+
+- Download the Task09_Spleen zip file
+- Decompressed the file
+- Write the full path in the **input** flag in the [train file](./train.py)
+
+
+#### [DeepEdit Training](./train.py)
+
+This is an extension for [train.py](./train.py) that redefines basic default arguments to run 3D training.
+
+```bash
+# Run to know all possible options
+python ./train.py -h
+
+# Train a DeepEdit model
+python ./train_3d.py
+    --input       deepedit/Task09_Spleen \
+    --output      deepedit_model/ \
+    --epochs      100
+
+# After training to export/save as torch script model
+python ./train.py
+    --input       deepedit_model/model.pt \
+    --output      deepedit_model/model.ts \
+    --export      true
+```
+
+#### [DeepEdit Inference](./inference.ipynb)
+
+This notebook helps to run any pre-transforms before running inference over a DeepEdit single label model.
+It also helps to run post-transforms to get the final label mask.
+
+
+#### DeepEdit Stats
+
+By-default Tensorboard handlers are added as part of training/validation steps.