Add validated CT/SEG tensor and patient-split tutorial

README.md

@@ -50,6 +50,7 @@ Zenodo permanent citeable record: https://zenodo.org/records/22691908.
 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.
+* [Loading paired CT/DICOM SEG objects as Pytorch tensors](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/ct_seg_to_tensors.ipynb): select referenced regular-grid C4KC-KiTS pairs with idc-index's seg_index, load HU volumes and associated segmentation mask with highdicom, and convert to matched PyTorch tensors.
 * [Working with IDC clinical data](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/clinical_data_intro.ipynb): this tutorial will help you become familiar with how clinical and other tabular data accompanying images in IDC is organized, and how you can use it to build cohorts.
 * [Direct access to tags/frames from GCS/AWS buckets](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/gcs_aws_direct_access.ipynb): learn how to access individual frames or tags of large DICOM files from the bucket without having to download the entire file (this notebook accompanies documentation article here: https://learn.canceridc.dev/data/downloading-data/direct-loading)
 * [Using DICOMweb to access IDC data](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/advanced_topics/idc_dicomweb_access.ipynb): both metadata and pixel data can be access using DICOMweb, which is particularly important while working with digital pathology, as it enables granular access to the individual pyramid tiles (frames)