Merge branch 'ImagingDataCommons:master' into pathology_notebook_update

Author: DanielaSchacherer

README.md

@@ -4,7 +4,7 @@ This repository contains tutorial materials (for the most part, as Python notebo
 
 If this is the first time you hear about IDC, you may want to check out our [Getting Started documentation page](https://learn.canceridc.dev/getting-started-with-idc). Here are some highlights about what IDC has to offer:
 
-* **>65 TB of data**: IDC contains radiology, brightfield (H&E) and fluorescence slide microscopy images, along with image-derived data (annotations, segmentations, quantitative measurements) and accompanying clinical data
+* **>78 TB of data**: IDC contains radiology, brightfield (H&E) and fluorescence slide microscopy images, along with image-derived data (annotations, segmentations, quantitative measurements) and accompanying clinical data
 
 * **free**: all of the data in IDC is publicly available: no registration, no access requests
 
@@ -18,31 +18,45 @@ The tutorial notebooks are located in the [notebooks](https://github.com/Imaging
 
 ## [`getting_started`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/getting_started)
 
-"Getting Started" notebooks are intended to introduce the users to IDC. We believe those notebooks are the best place to start using IDC. In this notebook series you will learn:
-* how IDC data is organized
-* how to search IDC data
-* how to download data from IDC
-* how to use various visualization tools with IDC data
-* how to properly acknowledge data contributors and stay compliant with the usage license
+"Getting Started" python notebooks are intended to introduce the users to IDC. 
+
+* [Basics of using IDC data programmatically](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/part2_searching_basics.ipynb): learn how to use `idc-index` python package to programmatically search and download IDC data, visualize images and annotations, build cohorts and checking acknowledgments and liceses for the data included in your cohort.
+* [Searching clinical data](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/exploring_clinical_data.ipynb): identify clinical and other non-imaging data accompanying imaging collections in IDC using `idc-index` python package and `duckdb`.
+* [Advanced searching using BigQuery](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/part3_exploring_cohorts.ipynb): access all of the metadata to build comprehensive queries and detailed cohort selection criteria.
 
 ## [`advanced_topics`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/advanced_topics)
 
-Notebooks in this folder focus on topics that will require understanding of the basics, and aim to address more narrow use cases of IDC usage. Such topics include:
-* how to search clinical data accompanying IDC images and how to combine imaging and clinical metadata in your searches
-* how to use AWS-specific components for working with IDC data
-* how to deploy open source OHIF and Slim viewers using free Google Cloud resources
+Notebooks in this folder focus on topics that will require understanding of the basics, and aim to address more narrow use cases of IDC usage. 
+
+* [Searching DICOM private tags](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/dicom_private_tags_intro.ipynb): all of DICOM attributes for the imaging data in IDC are searchable using BigQuery. DICOM private tags often contain critical information, such as diffusion b-values, but are a bit more tricky to access from BigQuery. In this tutorial you will learn how to accomplish this.
+* [Using BigQuery for searching IDC clinical data](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/clinical_data_intro.ipynb): BigQuery is an alternative to `idc-index` and `duckdb` for searching clinical data. This tutorial demonstrates more capabilities compared to the introductory clinical data usage tutorial.
+
+## [`viewers_deployment`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/viewers_deployment)
+
+These notebooks can be used to deploy your own cloud-based instance of OHIF or Slim viewers using Google Firebase, which you can use to visualize analysis results you generated for IDC data, or to work with your own images. These tutorials utilize free tier of Firebase, and so there is no cost to keep the deployed viewers available in the cloud.
+
+* [OHIF Firebase deployment](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/viewers_deployment/OHIF_FireBase_deployment.ipynb)
+* [Slim Firebase deployment](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/viewers_deployment/slim_Firebase_deployment.ipynb)
+* [Setting up Google Healthcare DICOM store](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/viewers_deployment/Creating_Google_Healthcare_DICOM_store.ipynb): once you have your viewers deployed, you can use this tutorial to create a DICOM store with your data, which you can then access from the viewers deployed
 
 ## [`collectons_demos`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos)
 
 This folders contains notebooks that demonstrate the usage of the data in the specific IDC collections. The notebooks in this folder will always have the prefix of the `collection_id` they correspond to, for easier navigation.
 
+* [Using hiplot for exploring prostate MRI in IDC](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/collections_demos/prostate-MRI_hiplot_experiments.ipynb): this notebook demonstrates how [`hiplot`](https://facebookresearch.github.io/hiplot/), an open source package for high-dimensional parameter visualization, for examining various MRI acquisition parameters for the prostate MRI images available in IDC.
+* [Visible Human Project exploration](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/collections_demos/nlm_visible_human_project.ipynb): demonstration of searching and visualizing images from the National Library of Medicine Visible Human Project available on IDC.
+* [RMS-Mutation-Prediction collection exploration](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction): notebooks in this folder demonstrate selecting images from the `RMS-Mutation-Prediction` collection based on various attributes of images and expert annotations.
+
 ## [`pathomics`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/pathomics)
 
 This folder is dedicated to the notebooks focused on the digital pathology (pathomics) applications. The use of DICOM standard is relatively new in digital pathology, and this field is being actively developed, thus a dedicated folder for this.
+* [Getting started with pathology images in IDC](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/pathomics/getting_started_with_digital_pathology.ipynb): all of the pathology images in IDC are in DICOM Slide Microscopy format; this notebook will help you get started with using this representation and also searching IDC pathology images.
 
 ## [`analysis`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/analysis)
 
 Demonstrations/examples of analyses of images from IDC.
+* [MedSAM on IDC](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/analysis/MedSAM_with_IDC.ipynb): learn how to experiment with MedSAM on the images available from IDC.
+* [MHub.ai with IDC data](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/analysis/mhubai_tutorial.ipynb): [MHub.ai](https://mhub.ai) is a platform for Deep Learning models in medical imaging, which are interoperable with IDC and can be applied directly to the IDC DICOM images. Learn how to get started from this notebook!
 
 ## [`labs`](https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/labs)