# Downloading the histopathology slides

**URL:** <https://discourse.canceridc.dev/t/downloading-the-histopathology-slides/780>\
**Category:** Support\
**Created:** [February 26, 2026, 5:28pm UTC](https://discourse.canceridc.dev/t/downloading-the-histopathology-slides/780 "2026-02-26T17:28:16Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![fedorov](https://sea1.discourse-cdn.com/flex015/user_avatar/discourse.canceridc.dev/fedorov/32/3_2.png) [@fedorov](https://discourse.canceridc.dev/u/fedorov)\
**Post date:** [February 26, 2026, 7:21pm UTC](https://discourse.canceridc.dev/t/downloading-the-histopathology-slides/780/2 "2026-02-26T19:21:27Z")

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Hi, and welcome to IDC!

The main thing to know upfront: IDC histopathology slides are stored in **DICOM Slide Microscopy (SM)** format, and each slide downloads as a **folder of `.dcm` files** rather than a single SVS or TIFF. This is expected — the files represent the different resolution levels of the image pyramid. Tools like [wsidicom](https://github.com/imi-bigpicture/wsidicom), [TIAToolbox](https://github.com/TissueImageAnalytics/tiatoolbox) (`DICOMWSIReader`), and [QuPath](https://qupath.github.io/) (v0.4+) all handle this natively by pointing them at the folder.

To find and download slides, the [`idc-index`](https://github.com/ImagingDataCommons/idc-index) Python package is the easiest starting point (no authentication needed):

```python
from idc_index import IDCClient
client = IDCClient()

# Find slide microscopy series (here: TCGA-LUAD collection)
slides = client.sql_query("""
    SELECT i.PatientID, i.SeriesInstanceUID, i.series_size_MB
    FROM index i
    WHERE i.collection_id = 'tcga_luad' AND i.Modality = 'SM'
""")

# Download — each series lands in its own subfolder
client.download_from_selection(
    seriesInstanceUID=slides["SeriesInstanceUID"].tolist(),
    downloadDir="./slides",
    dirTemplate="%collection_id/%PatientID/%SeriesInstanceUID"
)

```

Note that slides can be several GB each, so it’s worth checking total size (`SUM(series_size_MB)`) before kicking off a large download.

For a fuller introduction to searching and working with IDC pathology data, the [Getting started with digital pathology](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/pathomics/getting_started_with_digital_pathology.ipynb) notebook (runnable for free on Colab) is a good next step. You may also be interested in this recent post: [Get started with TIAToolbox to analyze IDC pathology images](https://discourse.canceridc.dev/t/get-started-with-tiatoolbox-to-analyze-idc-pathology-images/779).

Finally, really the easiest interface to navigate IDC and learn how to use its content is the Claude skill we announced in this post: [Imaging Data Commons Claude skill launched!](https://discourse.canceridc.dev/t/imaging-data-commons-claude-skill-launched/777)

Feel free to follow up here with any questions!

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