Moved text cells above code cells

Author: DanielaSchacherer

notebooks/collections_demos/bonemarrowwsi_pediatricleukemia.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "toc_visible": true,
-      "authorship_tag": "ABX9TyOo2IUHQcuW5Dtanxe/Wiqr",
+      "authorship_tag": "ABX9TyMp4/gGxQcu3ybm3Suc/Pv5",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -787,6 +787,20 @@
         "id": "WHvZRtVE5ByF"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The following two code cells define and use `get_roi_annotations()` to select all DICOM ANNs in the `BoneMarrowWSI-PediatricLeukemia` collection that contain ROI annotations of the monolayer area.\n",
+        "The resulting pandas DataFrame contains\n",
+        "- **'reference_SeriesInstanceUID'** and **'reference_SOPInstanceUID'**: the SeriesInstanceUID and SOPInstanceUID of the slide level the annotations refer to\n",
+        "- **'roi_id'**: the ID of the ROI\n",
+        "- **'roi_label'**: its label   \n",
+        "- **'roi_coordinates'**: the 2D coordinates in the image coordinate system of the referenced slide level.\n"
+      ],
+      "metadata": {
+        "id": "XKzx9B1fU9ed"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -845,20 +859,6 @@
       "execution_count": 21,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The following cell uses `get_roi_annotations()` to select all DICOM ANNs in the `BoneMarrowWSI-PediatricLeukemia` collection that contain ROI annotations of the monolayer area.\n",
-        "The resulting pandas DataFrame contains\n",
-        "- **'reference_SeriesInstanceUID'** and **'reference_SOPInstanceUID'**: the SeriesInstanceUID and SOPInstanceUID of the slide level the annotations refer to\n",
-        "- **'roi_id'**: the ID of the ROI\n",
-        "- **'roi_label'**: its label   \n",
-        "- **'roi_coordinates'**: the 2D coordinates in the image coordinate system of the referenced slide level.\n"
-      ],
-      "metadata": {
-        "id": "XKzx9B1fU9ed"
-      }
-    },
     {
       "cell_type": "code",
       "source": [
@@ -1317,6 +1317,23 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The following code cells define and use `get_cell_annotations()` to select all DICOM ANNs in the `BoneMarrowWSI-PediatricLeukemia` collection that contain cell annotations. By setting the parameter 'subset' to either 'labeled', 'unlabeled' or 'both', it's possible to extract either only labeled, unlabeled or all cell annotations.\n",
+        "The resulting pandas DataFrame contains\n",
+        "- **'reference_SeriesInstanceUID'** and **'reference_SOPInstanceUID'**: the SeriesInstanceUID and SOPInstanceUID of the slide level the annotations refer to\n",
+        "- **'annotation_session'**: 'n/a' for the unlabeled cells, otherwise the number of the annotation session or 'consensus' for the final consensus.\n",
+        "- **'cell_id'**: the ID of the cell\n",
+        "- **'roi_id'**: if applicable, the ID of the monolayer ROI, the cell is located within\n",
+        "- **'cell_label_code_scheme'**: Tuple of code of the cell label and designator of the coding scheme, e.g. (414387006, SCT) which is code 414387006 from SNOMED CT ontology\n",
+        "- **'cell_label'**: Code meaning of the cell label e.g. 'Structure of haematological system'\n",
+        "- **'cell_coordinates'**: the 2D coordinates in the image coordinate system of the referenced slide level"
+      ],
+      "metadata": {
+        "id": "gpDVBwjYWz-w"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1381,6 +1398,7 @@
         "    cells = pd.DataFrame(rows)\n",
         "    return cells\n",
         "\n",
+        "\n",
         "def get_annotation_session(ann: hd.ann.sop.MicroscopyBulkSimpleAnnotations) -> str:\n",
         "    if 'unlabeled' in ann.SeriesDescription.lower():\n",
         "        return 'n/a'\n",
@@ -1392,23 +1410,6 @@
       "execution_count": 22,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The following cell uses `get_cell_annotations()` to select all DICOM ANNs in the `BoneMarrowWSI-PediatricLeukemia` collection that contain cell annotations. By setting the parameter 'subset' to either 'labeled', 'unlabeled' or 'both', it's possible to extract either only labeled, unlabeled or all cell annotations.\n",
-        "The resulting pandas DataFrame contains\n",
-        "- **'reference_SeriesInstanceUID'** and **'reference_SOPInstanceUID'**: the SeriesInstanceUID and SOPInstanceUID of the slide level the annotations refer to\n",
-        "- **'annotation_session'**: 'n/a' for the unlabeled cells, otherwise the number of the annotation session or 'consensus' for the final consensus.\n",
-        "- **'cell_id'**: the ID of the cell\n",
-        "- **'roi_id'**: if applicable, the ID of the monolayer ROI, the cell is located within\n",
-        "- **'cell_label_code_scheme'**: Tuple of code of the cell label and designator of the coding scheme, e.g. (414387006, SCT) which is code 414387006 from SNOMED CT ontology\n",
-        "- **'cell_label'**: Code meaning of the cell label e.g. 'Structure of haematological system'\n",
-        "- **'cell_coordinates'**: the 2D coordinates in the image coordinate system of the referenced slide level"
-      ],
-      "metadata": {
-        "id": "gpDVBwjYWz-w"
-      }
-    },
     {
       "cell_type": "code",
       "source": [
@@ -2426,7 +2427,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## How to use the `BoneMarrowWSI-PediatricLeukemia` annotations"
+        "## How to use the `BoneMarrowWSI-PediatricLeukemia` annotations\n"
       ],
       "metadata": {
         "id": "eKSnU4dyXkiR"