updated intro

Author: fedorov

notebooks/collections_demos/RMS-Mutation-Prediction-Expert-Annotations_exploration.ipynb

@@ -16,7 +16,8 @@
         "id": "bDUoVEfF0yzc"
       },
       "source": [
-        "# Working with DICOM Structured Reports in computational pathology"
+        "# Exploration of RMS-Mutation-Prediction-Expert-Annotations collection: Working with DICOM Structured Reports in computational pathology\n",
+        "\n"
       ]
     },
     {
@@ -29,13 +30,27 @@
         "\n",
         "---\n",
         "\n",
-        "There are different ways to store annotations in DICOM, depending on the type of the annotation, and the specific DICOM object used. In this notebook we will discuss how to use planar closed contour annotations stored in [DICOM Structured Report](https://dicom.nema.org/dicom/2013/output/chtml/part20/sect_A.3.html) (SR) documents. Using the example of expert-generated region annotations in rhabdomyosarcoma tumour slides, which have [recently been ingested into the NCI Imaging Data Commons](https://datascience.cancer.gov/news-events/news/rhabdomyosarcoma-images-now-available-through-nci-imaging-data-commons) (IDC), the organization of annotations stored in such SR objects is explained, and examples of how those annotations can be used and combined with the respective DICOM whole-slide images (WSIs) are provided.\n",
+        "`RMS-Mutation-Prediction-Expert-Annotations` is collection available in the [NCI Imaging Data Commons (IDC)](https://portal.imaging.datacommons.cancer.gov) that contains expert annotations of tissue types for 95 patients of the digital pathology slide images in the `RMS-Mutation-Prediction` collection released earlier. You can learn more about this collection in the following dataset record:\n",
+        "\n",
+        "> Bridge, C., Brown, G. T., Jung, H., Lisle, C., Clunie, D., Milewski, D., Liu, Y., Collins, J., Linardic, C. M., Hawkins, D. S., Venkatramani, R., Fedorov, A., & Khan, J. (2024). Expert annotations of the tissue types for the RMS-Mutation-Prediction microscopy images [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10462858\n",
+        "\n",
+        "You can access this annotations collection in the IDC Portal using [this link](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations), or you can explore its content using this [custom Google Looker dashboard](https://tinyurl.com/idc-rms-annotations).\n",
+        "\n",
+        "As is the case with all of the content of IDC, both the images and annotations are publicly available and are free to download!\n",
+        "\n",
+        "In this notebook we give you an overview of this collection, and demonstrate how to navigate its content programmatically.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "There are different ways to store annotations in DICOM, depending on the type of the annotation, and the specific DICOM object used. In this notebook we will discuss how to use planar closed contour annotations stored in [DICOM Structured Report](https://dicom.nema.org/dicom/2013/output/chtml/part20/sect_A.3.html) (SR) documents, which is the representation of annotations adopted in the `RMS-Mutation-Prediction-Expert-Annotations` collection. Using the example of expert-generated region annotations in rhabdomyosarcoma tumour slides, which have [recently been ingested into the NCI Imaging Data Commons](https://datascience.cancer.gov/news-events/news/rhabdomyosarcoma-images-now-available-through-nci-imaging-data-commons) (IDC), the organization of annotations stored in such SR objects is explained, and examples of how those annotations can be used and combined with the respective DICOM whole-slide images (WSIs) are provided.\n",
         "\n",
         "If you have any questions about this tutorial, please ask them on IDC forum: https://discourse.canceridc.dev\n",
         "\n",
         "---\n",
         "\n",
-        "Initial version: May 2024"
+        "Initial version: May 2024\n",
+        "\n",
+        "Updated: June 2024"
       ]
     },
     {
@@ -3616,12 +3631,12 @@
         "selection_df"
       ],
       "metadata": {
-        "id": "FeMBDY1PY841",
-        "outputId": "831da40d-2592-4429-8379-ed5a9bb552a9",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 424
-        }
+        },
+        "id": "FeMBDY1PY841",
+        "outputId": "831da40d-2592-4429-8379-ed5a9bb552a9"
       },
       "execution_count": 3,
       "outputs": [