17 Add load medical image notebook (#19)

* [DLMED] add load images notebook

* [DLMED] update notebook content

README.md

@@ -31,6 +31,8 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist
 #### [integrate_3rd_party_transforms](./integrate_3rd_party_transforms.ipynb)
 This tutorial shows how to integrate 3rd party transforms into MONAI program.
 Mainly shows transforms from BatchGenerator, TorchIO, Rising and ITK.
+#### [load_medical_imagesl](./load_medical_images.ipynb)
+This notebook introduces how to easily load different formats of medical images in MONAI and execute many additional operations.
 #### [mednist_GAN_tutorial](./mednist_GAN_tutorial.ipynb)
 This notebook illustrates the use of MONAI for training a network to generate images from a random input tensor.
 A simple GAN is employed to do with a separate Generator and Discriminator networks.

automatic_mixed_precision.ipynb

@@ -673,6 +673,25 @@
     "plt.legend(loc='best')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup data directory\n",
+    "\n",
+    "Remove directory if a temporary was used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if directory is None:\n",
+    "    shutil.rmtree(root_dir)"
+   ]
   }
  ],
  "metadata": {

brats_segmentation_3d.ipynb

@@ -727,7 +727,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,