text updates

Signed-off-by: neuronflow <florian.kofler@tum.de>

Author: neuronflow

AURORA/tutorial.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +129,7 @@
    "source": [
     "## Data\n",
     "\n",
-    "Aurora expects preprocessed input data as NIfTI file or NumPy Array.  \n",
+    "AURORA expects `preprocessed` input data as NIfTI file or NumPy Array, `preprocessed` meaning the files should be co-registerend, skullstripped and in SRI-24 space.\n",
     "If your data is not preprocessed yet, consider using our [BrainLes preprocessing](https://github.com/BrainLesion/preprocessing) package (or its predecessor [BraTS-Toolkit](https://github.com/neuronflow/BraTS-Toolkit)).\n",
     "\n",
     "In this example we provide sample data from the [ASNR-MICCAI BraTS Brain Metastasis Challenge](https://www.synapse.org/#!Synapse:syn51156910/wiki/622553), which is already preprocessed."
@@ -146,62 +146,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Minimal example using default settings and only T1 as input"
+    "### Minimal example using default settings and only T1c as input"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-01-30 10:56:59 INFO: Initialized AuroraGPUInferer with config: AuroraInfererConfig(log_level=20, tta=True, sliding_window_batch_size=1, workers=0, threshold=0.5, sliding_window_overlap=0.5, crop_size=(192, 192, 32), model_selection=<ModelSelection.BEST: 'best'>)\n",
-      "2024-01-30 10:56:59 INFO: Set torch device: cuda\n",
-      "2024-01-30 10:56:59 INFO: Infer with config: AuroraInfererConfig(log_level=20, tta=True, sliding_window_batch_size=1, workers=0, threshold=0.5, sliding_window_overlap=0.5, crop_size=(192, 192, 32), model_selection=<ModelSelection.BEST: 'best'>) and device: cuda\n",
-      "2024-01-30 10:56:59 INFO: Successfully validated input images. Input mode: NIFTI_FILEPATH\n",
-      "2024-01-30 10:56:59 INFO: Received files: T1: True, T1C: False, T2: False, FLAIR: False\n",
-      "2024-01-30 10:56:59 INFO: Inference mode: t1-o\n",
-      "2024-01-30 10:56:59 INFO: No loaded compatible model found. Loading Model and weights\n",
-      "2024-01-30 10:56:59 INFO: Setting up Dataloader\n",
-      "2024-01-30 10:56:59 INFO: Running inference on device := cuda\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BasicUNet features: (32, 32, 64, 128, 256, 32).\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-01-30 10:57:05 INFO: Applying test time augmentations\n",
-      "2024-01-30 10:58:29 INFO: Post-processing data\n",
-      "2024-01-30 10:58:29 INFO: Saving post-processed data as NIFTI files\n",
-      "2024-01-30 10:58:29 INFO: Saved segmentation to output/segmentation.nii.gz\n",
-      "2024-01-30 10:58:29 INFO: Returning post-processed data as Dict of Numpy arrays\n",
-      "2024-01-30 10:58:29 INFO: Finished inference \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from brainles_aurora.inferer import AuroraGPUInferer, AuroraInferer, AuroraInfererConfig\n",
     "\n",
     "config = AuroraInfererConfig(\n",
-    "    tta=False\n",
-    ")  # Disable test time augmentations for a quick demo, should be set to True for better results\n",
-    "\n",
+    "    tta=False  # Disable test time augmentations for a quick demo, should be set to True for better results\n",
+    ")\n",
     "# If you don-t have a GPU that supports CUDA use the CPU version: AuroraInferer(config=config)\n",
-    "inferer = AuroraGPUInferer(config=config)\n",
+    "inferer = AuroraGPUInferer(\n",
+    "    config=config,\n",
+    "    cuda_devices=\"0\",  # optional, if you have multiple GPUs you can specify which one to use\n",
+    ")\n",
     "\n",
-    "_ = inferer.infer(\n",
-    "    t1=\"data/t1n.nii.gz\",\n",
+    "inferer.infer(\n",
+    "    t1c=\"data/t1c.nii.gz\",\n",
     "    segmentation_file=\"output/segmentation.nii.gz\",\n",
     ")"
    ]
@@ -401,6 +367,11 @@
     "results = inferer.infer(t1=t1_np)\n",
     "print([f\"{k} : {v.shape}\" for k, v in results.items()])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {