diff --git a/README.md b/README.md
index b71be91c..a5045410 100644
--- a/README.md
+++ b/README.md
@@ -109,6 +109,10 @@ Here you find a series of notebooks providing an overview of the core features o
 
 These are examples of how DeepTrack2 can be used on real datasets:
 
+- DTEx207 **[Gan Image Generation](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb)** <a href="https://colab.research.google.com/github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+
+  Genereting realistic electron microscopy images with a conditional GAN
+
 - DTEx211 **[MNIST](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx211_MNIST.ipynb)** <a href="https://colab.research.google.com/github/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx211_MNIST.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a>
 
   Training a fully connected neural network to identify handwritten digits using MNIST dataset.
@@ -158,10 +162,6 @@ These are examples of how DeepTrack2 can be used on real datasets:
 
   Tracking multiple particles in 3D for holography.
 
-- DTEx217 **GAN image generation**
-
-  Using a GAN to create cell image from masks.
-
 Specific examples for label-free particle tracking using **LodeSTAR**:
 
 - DTEx231A **LodeSTAR to Detect Particles** <!-- **[LodeSTAR to Detect Particles](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx231A_LodeSTAR_template.ipynb)** <a href="https://colab.research.google.com/github/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx231A_LodeSTAR_template.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> -->
diff --git a/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb b/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb
index 3a9f2fb9..3fa6acbe 100644
--- a/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb
+++ b/tutorials/2-examples/DTEx207_GAN_image_generation.ipynb
@@ -1,17 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "512bca8a",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "# TODO: Complete example"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1c61223d",
@@ -32,12 +20,22 @@
     "# !pip install deeptrack  # Uncomment if running on Colab/Kaggle."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "005cac6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install deeplay  # Uncomment if needed"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04e6451e",
    "metadata": {},
    "source": [
-    "In this tutorial, you will train a conditional GAN to translate segmentation masks into realistic electron microscopy images. The dataset contains raw ssTEM images paired with segmentation maps that identify structures in the tissue.\n",
+    "In this tutorial, you will train a conditional GAN to translate segmentation masks into realistic electron microscopy images. The dataset contains raw serial section transmission electron microscopy (ssTEM) images paired with segmentation maps that identify structures in the tissue.\n",
     "\n",
     "You will prepare the dataset, build the generator and discriminator, train the GAN to generate images, and finally evaluate the model by comparing generated images with the ground truth."
    ]
@@ -163,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "cbedf5da",
    "metadata": {},
    "outputs": [],
@@ -175,7 +173,7 @@
     "           >> dt.Lambda(select_labels, class_labels=[255, 191]))\n",
     " \n",
     "pip = ((seg_pip & im_pip) >> dt.FlipLR(sources.flip_lr)\n",
-    "       >> dt.FlipUD(sources.flip_ud) >> dt.Crop(crop=(256, 256, 3))\n",
+    "       >> dt.FlipUD(sources.flip_ud) >> dt.Crop(crop=(256, 256, 3), time_consistent=True)\n",
     "       >> dt.MoveAxis(2, 0) >> dt.pytorch.ToTensor(dtype=torch.float))"
    ]
   },