Merge pull request #30 from BrainLesion/29-two-minor-changes-in-auroratutorialipynb

- fix wrong work dir and filenames

Author: neuronflow

AURORA/output/t1c_segmentation.nii.gz

@@ -19,7 +19,7 @@
     "\n",
     "Now that you are visualizing the notebook in Colab, run the next cell to install the packages we will use. There are few things you should follow in order to properly set the notebook up:\n",
     "1. Warning: This notebook was not authored by Google. Click on 'Run anyway'.\n",
-    "1. When the installation commands are done, there might be \"Restart runtime\" button at the end of the output. Please, click it."
+    "1. When the installation commands are done, there might be a \"Restart runtime\" button at the end of the output. Please, click it."
    ]
   },
   {
@@ -75,7 +75,10 @@
     "import sys\n",
     "\n",
     "COLAB_BASE_PATH = \"/content/tutorials/AURORA/\"\n",
-    "sys.path.insert(0, COLAB_BASE_PATH)"
+    "sys.path.insert(0, COLAB_BASE_PATH)\n",
+    "\n",
+    "# cd into AURORA folder\n",
+    "%cd tutorials/AURORA"
    ]
   },
   {
@@ -335,10 +338,10 @@
     "\n",
     "# Use all four input modalities,we also create other outputs and a custom log file\n",
     "_ = inferer.infer(\n",
-    "    t1=\"data/t1n.nii.gz\",\n",
+    "    t1=\"data/t1.nii.gz\",\n",
     "    t1c=\"data/t1c.nii.gz\",\n",
-    "    t2=\"data/t2w.nii.gz\",\n",
-    "    fla=\"data/t2f.nii.gz\",\n",
+    "    t2=\"data/t2.nii.gz\",\n",
+    "    fla=\"data/flair.nii.gz\",\n",
     "    segmentation_file=\"output/multi-modal_segmentation.nii.gz\",\n",
     "    # The unbinarized network outputs for the whole tumor channel (edema + enhancing tumor core + necrosis) channel\n",
     "    whole_tumor_unbinarized_floats_file=\"output/whole_tumor_unbinarized_floats.nii.gz\",\n",
@@ -409,7 +412,7 @@
     "inferer = AuroraGPUInferer(config=config)\n",
     "\n",
     "# we load the nifty data to a numpy array\n",
-    "t1_np = nib.load(\"data/t1n.nii.gz\").get_fdata()\n",
+    "t1_np = nib.load(\"data/t1.nii.gz\").get_fdata()\n",
     "\n",
     "# we can now use the inferer to perform the inference and obtain again a numpy array containing the segmentation\n",
     "results = inferer.infer(t1=t1_np)\n",