update 3d_registration tutorial (#806)

Author: KumoLiu

3d_registration/paired_lung_ct.ipynb

@@ -129,13 +129,11 @@
     "from monai.networks.blocks import Warp\n",
     "from monai.networks.nets import LocalNet\n",
     "from monai.transforms import (\n",
-    "    AddChanneld,\n",
     "    Compose,\n",
     "    LoadImaged,\n",
     "    RandAffined,\n",
     "    Resized,\n",
     "    ScaleIntensityRanged,\n",
-    "    EnsureTyped,\n",
     ")\n",
     "from monai.utils import set_determinism, first\n",
     "\n",
@@ -302,12 +300,10 @@
    "source": [
     "## Setup transforms for training and validation\n",
     "Here we use several transforms to augment the dataset:\n",
-    "1. LoadImaged loads the lung CT images and labels from NIfTI format files.\n",
-    "2. AddChanneld as the original data doesn't have channel dim, add 1 dim to construct \"channel first\" shape.\n",
-    "3. ScaleIntensityRanged extracts intensity range [-285, 3770] and scales to [0, 1].\n",
-    "4. RandAffined efficiently performs rotate, scale, shear, translate, etc. together based on PyTorch affine transform.\n",
-    "5. Resized resize images to the same size.\n",
-    "6. EnsureTyped converts the numpy array to PyTorch Tensor for further steps."
+    "1. LoadImaged loads the lung CT images and labels from NIfTI format files. \"ensure_channel_first=True\" ensure that the first dim is channel.\n",
+    "2. ScaleIntensityRanged extracts intensity range [-285, 3770] and scales to [0, 1].\n",
+    "3. RandAffined efficiently performs rotate, scale, shear, translate, etc. together based on PyTorch affine transform.\n",
+    "4. Resized resize images to the same size."
    ]
   },
   {
@@ -324,10 +320,8 @@
     "train_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
-    "        ),\n",
-    "        AddChanneld(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
+    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"],\n",
+    "            ensure_channel_first=True\n",
     "        ),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"fixed_image\", \"moving_image\"],\n",
@@ -345,18 +339,13 @@
     "            align_corners=(True, True, None, None),\n",
     "            spatial_size=(96, 96, 104)\n",
     "        ),\n",
-    "        EnsureTyped(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
-    "        ),\n",
     "    ]\n",
     ")\n",
     "val_transforms = Compose(\n",
     "    [\n",
     "        LoadImaged(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
-    "        ),\n",
-    "        AddChanneld(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
+    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"],\n",
+    "            ensure_channel_first=True\n",
     "        ),\n",
     "        ScaleIntensityRanged(\n",
     "            keys=[\"fixed_image\", \"moving_image\"],\n",
@@ -369,9 +358,6 @@
     "            align_corners=(True, True, None, None),\n",
     "            spatial_size=(96, 96, 104)\n",
     "        ),\n",
-    "        EnsureTyped(\n",
-    "            keys=[\"fixed_image\", \"moving_image\", \"fixed_label\", \"moving_label\"]\n",
-    "        ),\n",
     "    ]\n",
     ")"
    ]
@@ -693,6 +679,7 @@
     "\n",
     "                val_ddf, val_pred_image, val_pred_label = forward(\n",
     "                    val_data, model)\n",
+    "                val_pred_label[val_pred_label > 1] = 1\n",
     "\n",
     "                val_fixed_image = val_data[\"fixed_image\"].to(device)\n",
     "                val_fixed_label = val_data[\"fixed_label\"].to(device)\n",
@@ -723,6 +710,7 @@
     "        optimizer.zero_grad()\n",
     "\n",
     "        ddf, pred_image, pred_label = forward(batch_data, model)\n",
+    "        pred_label[pred_label > 1] = 1\n",
     "\n",
     "        fixed_image = batch_data[\"fixed_image\"].to(device)\n",
     "        fixed_label = batch_data[\"fixed_label\"].to(device)\n",
@@ -1281,7 +1269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,