README.md

---
pretty_name: NCI Imaging Data Commons (IDC) index
license:
  - cc-by-4.0
  - cc-by-3.0
  - cc-by-nc-4.0
  - cc-by-nc-3.0
language:
  - en
tags:
  - medical
  - cancer
  - dicom
  - radiology
  - pathology
  - catalog
  - imaging-data-commons
size_categories:
  - 1M<n<10M
configs:
  - config_name: idc_index
    data_files: idc_index.parquet
    default: true
  - config_name: analysis_results_index
    data_files: analysis_results_index.parquet
  - config_name: ann_group_index
    data_files: ann_group_index.parquet
  - config_name: ann_index
    data_files: ann_index.parquet
  - config_name: clinical_index
    data_files: clinical_index.parquet
  - config_name: collections_index
    data_files: collections_index.parquet
  - config_name: contrast_index
    data_files: contrast_index.parquet
  - config_name: ct_index
    data_files: ct_index.parquet
  - config_name: mr_index
    data_files: mr_index.parquet
  - config_name: pt_index
    data_files: pt_index.parquet
  - config_name: rtstruct_index
    data_files: rtstruct_index.parquet
  - config_name: seg_index
    data_files: seg_index.parquet
  - config_name: sm_index
    data_files: sm_index.parquet
  - config_name: sm_instance_index
    data_files: sm_instance_index.parquet
  - config_name: version_metadata_index
    data_files: version_metadata_index.parquet
  - config_name: volume_geometry_index
    data_files: volume_geometry_index.parquet
---

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# NCI Imaging Data Commons (IDC) index

**This dataset is a catalog. It contains metadata and cloud locations for every
DICOM series in the NCI Imaging Data Commons; it does not contain pixel data.**

[IDC](https://portal.imaging.datacommons.cancer.gov) is an NCI Cancer Research Data Commons repository of
publicly available cancer imaging data, co-located with analysis tools in the
cloud. To explore it interactively instead, use the
[IDC portal](https://portal.imaging.datacommons.cancer.gov/explore/). Without downloading anything, any image
in IDC can be [viewed in the browser](https://learn.canceridc.dev/portal/visualization). To query IDC in
plain language, point an AI assistant at its [agent interfaces](https://learn.canceridc.dev/ai-assistants/agents)
-- a hosted MCP server, an agent skill, and a REST API over the same metadata.

This catalog describes IDC v25 (released 2026-09-26): **1,044,191 series** across 168,507
studies, 86,362 patients and 179 collections, totalling
**99.9 TB** of imaging data.

One row is one DICOM series, with its collection, patient, study and series
attributes, its license and source DOI, and the S3 URL to fetch it from. Use it
to find the data you want here, then download only that -- the alternative is
sifting through 100 TB.

These are the same Parquet files published with each
[idc-index-data release](https://github.com/ImagingDataCommons/idc-index-data/releases), republished here for the
dataset viewer, the SQL Console, automatic
[Croissant](https://huggingface.co/docs/dataset-viewer/en/croissant) metadata,
and a citable, versioned record you can pin.

Every config has a single split named `train`, the Hub default, because many
downstream tools assume it exists. It carries no train/test meaning.

## Quickstart

```bash
pip install datasets idc-index
```

`datasets` reads this catalog;
[`idc-index`](https://pypi.org/project/idc-index/) is the client that downloads
the DICOM files it points at. Filter here, download there:

```python
from datasets import load_dataset

idx = load_dataset("ImagingDataCommons/idc-index-data", "idc_index", split="train")
sel = idx.filter(
    lambda r: r["collection_id"] == "nsclc_radiomics" and r["Modality"] == "SEG"
)

from idc_index import IDCClient

client = IDCClient()
client.download_from_selection(
    seriesInstanceUID=sel["SeriesInstanceUID"], downloadDir="./idc_data"
)
```

Downloads come directly from IDC's public AWS and GCS buckets at no cost to you.
What lands on disk is DICOM;
[Loading images as tensors](#loading-images-as-tensors) below turns it into
arrays.

Every series in this catalog can also be looked at without downloading anything.
IDC streams the pixels to a zero-footprint browser viewer, and `get_viewer_URL`
builds a link to any series you have selected:

```python
print(client.get_viewer_URL(seriesInstanceUID=sel["SeriesInstanceUID"][0]))
```

It picks the viewer that fits the data -- [OHIF](https://github.com/OHIF/Viewers) for radiology,
[Slim](https://github.com/ImagingDataCommons/slim) for slide microscopy -- and opens the enclosing study with
your series selected. Passing a segmentation, as above, brings it up overlaid on
the images it segments.

Query the catalog without downloading anything, using DuckDB:

```sql
SELECT collection_id, COUNT(*) AS series, SUM(series_size_MB) / 1e6 AS size_TB
FROM 'hf://datasets/ImagingDataCommons/idc-index-data/idc_index.parquet'
GROUP BY 1 ORDER BY size_TB DESC LIMIT 10;
```

The same queries run in the **SQL Console** tab on this page, with no local
setup.

## Loading images as tensors

A DICOM series is not an array yet. For CT, MR and PET it is usually one file
per slice. The slices must be ordered by their position in space, not by file
name, and their stored values rescaled to physical units, such as Hounsfield
units for CT. Not every series is a volume at all: localizers, uneven slice
spacing and gantry tilt are all common.

[highdicom](https://highdicom.readthedocs.io/) handles this, and returns a
`Volume` that keeps voxel spacing and the patient-space affine next to the
array. The examples below were tested with highdicom 0.28.2.

```bash
pip install "highdicom>=0.28.2" duckdb idc-index torch
```

Start in the catalog. `volume_geometry_index` flags every CT, MR and PET series
whose slices form a regularly spaced 3D grid, so series that won't load as a
volume are never downloaded:

```python
import duckdb

hf = "hf://datasets/ImagingDataCommons/idc-index-data"
query = f"""
    SELECT SeriesInstanceUID
    FROM '{hf}/idc_index.parquet'
    JOIN '{hf}/volume_geometry_index.parquet' USING (SeriesInstanceUID)
    WHERE collection_id = 'nsclc_radiomics' AND Modality = 'CT'
      AND regularly_spaced_3d_volume
    LIMIT 3
"""
uids = [row[0] for row in duckdb.sql(query).fetchall()]
```

Download each series into its own directory, then load it:

```python
from pathlib import Path

import highdicom as hd
import numpy as np
import pydicom
import torch
from idc_index import IDCClient

client = IDCClient()
client.download_from_selection(
    seriesInstanceUID=uids, downloadDir="idc_data", dirTemplate="%SeriesInstanceUID"
)


def load_volume(uid):
    files = Path("idc_data", uid).glob("*.dcm")
    return hd.get_volume_from_series(
        [pydicom.dcmread(f) for f in files], dtype=np.float32
    )


def volume_to_channel_first_tensor(vol):
    # Convert to a tensor with a leading channel dimension, as is typically
    # required in pytorch

    # The result of a match_geometry operation may be permuted/flipped
    # so the resulting numpy array is non-contiguous
    arr = np.ascontiguousarray(vol.array)

    if vol.number_of_channel_dimensions == 0:
        # Volume has no channel -> add one
        # Result is (channels, slices, rows, columns)
        t = torch.from_numpy(arr).unsqueeze(0)
    elif vol.number_of_channel_dimensions == 1:
        # Volume has a trailing channel -> permute to the front
        t = torch.from_numpy(arr).permute([3, 0, 1, 2])
    else:
        raise ValueError("Expected at most one channel dimension")

    return t


vol = load_volume(uids[0])
image = volume_to_channel_first_tensor(vol)

print(image.shape, vol.spacing)  # spacing in mm, of the three spatial axes
```

`get_volume_from_series` raises `ValueError` for a series that is not a regular
grid. Those are the series the geometry filter above leaves out. Pass `dtype`
explicitly; the default is `float64`.

`get_volume_from_series` has other parameters, for example to choose which pixel
transforms are applied. See its
[documentation](https://highdicom.readthedocs.io/en/latest/package.html#highdicom.get_volume_from_series).

### Segmentations

`seg_index` has a row for each segmentation (DICOM SEG) series, and
`segmented_SeriesInstanceUID` names the image series it segments. This example
takes one from NLSTSeg, expert segmentations of lung lesions in NLST CT,
downloads it with its CT, and puts the mask on the CT's grid:

```python
query = f"""
    SELECT s.SeriesInstanceUID, s.segmented_SeriesInstanceUID
    FROM '{hf}/seg_index.parquet' s
    JOIN '{hf}/idc_index.parquet' i USING (SeriesInstanceUID)
    JOIN '{hf}/volume_geometry_index.parquet' g
      ON g.SeriesInstanceUID = s.segmented_SeriesInstanceUID
    WHERE i.analysis_result_id = 'nlstseg' AND s.total_segments > 1
      AND g.regularly_spaced_3d_volume
    LIMIT 1
"""
seg_uid, image_uid = duckdb.sql(query).fetchone()
client.download_from_selection(
    seriesInstanceUID=[seg_uid, image_uid],
    downloadDir="idc_data",
    dirTemplate="%SeriesInstanceUID",
)

ct = load_volume(image_uid)
seg = hd.seg.segread(next(Path("idc_data", seg_uid).glob("*.dcm")))
labels = seg.get_volume(combine_segments=True)
labels = labels.match_geometry(ct)

image = volume_to_channel_first_tensor(ct)
mask = volume_to_channel_first_tensor(labels)  # (1, slices, rows, columns)
print({s.SegmentNumber: s.SegmentLabel for s in seg.SegmentSequence})
```

A segmentation often covers fewer slices than its image, is rotated relative to
the source image, or stores the slices in the opposite order; `match_geometry`
pads, flips and rotates it (as required) onto the image grid. It raises
`RuntimeError` when the two grids are offset by a fraction of a voxel, and
rounding in stored positions alone can cause that: some `nsclc_radiomics`
segmentations sit 2e-5 voxels off their CT, just beyond the default tolerance,
and `match_geometry(ct, tol=1e-4)` accepts them. Loosen `tol` no further than
you need: a large tolerance can hide a segmentation that is genuinely misaligned
with its image.

`combine_segments=True` returns a label map, in which each voxel holds the
number of the segment it belongs to, or 0. NLSTSeg segments each lesion
separately, so `mask` numbers the lesions.

A label map cannot hold a voxel that belongs to two segments, and segments may
overlap. The expert segmentations in `nsclc_radiomics`, for example, outline the
primary tumor inside the lung that contains it, and `combine_segments=True`
raises `RuntimeError` on them. For those, `seg.get_volume()` returns one binary
mask per segment down the last axis, which `volume_to_channel_first_tensor`
moves to the front: `(segments, slices, rows, columns)`. Losses differ in which
form they take. PyTorch's `CrossEntropyLoss` takes class indices, as in a label
map, while others, such as the default behavior of MONAI's `DiceLoss`, take one
channel per segment. However, be aware that many common loss functions (such as
Dice) expect the segments to be non-overlapping even when using the
one-channel-per-segment representation, the so-called "one-hot" format.

`get_volume` can also select a subset of the segments. See its
[documentation](https://highdicom.readthedocs.io/en/latest/package.html#highdicom.seg.Segmentation.get_volume)
for this and its other parameters.

### Training and other image types

To feed a `DataLoader`, call `load_volume` from a `torch.utils.data.Dataset`.
Volumes differ in shape, so bring them to a common one before batching, for
example with `vol.pad_or_crop_to_spatial_shape((64, 256, 256))` or by
resampling.

- **Radiographs and mammograms** (CR, DX, MG) are one 2D image per file:
  `hd.imread(path).get_frame(1)`.
- **Slide microscopy** (SM) is a multi-resolution pyramid with one file per
  level, and the full-resolution level is usually too large for one array. Read
  a region of one level with `hd.imread(path).get_total_pixel_matrix()`, whose
  `row_start`, `row_end`, `column_start` and `column_end` bounds are 1-based,
  end excluded. `sm_instance_index` gives each file's `TotalPixelMatrixRows`,
  `TotalPixelMatrixColumns` and `PixelSpacing_0`, so you can pick the level
  before downloading.

## Indices

Each index is a separate config (subset). Load one with the `name` argument of
`load_dataset`, or select it from the dropdown in the dataset viewer.

| Config | Rows | Size | Description |
|---|---:|---:|---|
| `idc_index` (default) | 1,044,191 | 74.3 MB | This is the main metadata table provided by idc-index. Each row corresponds to a DICOM series, and contains attributes at the collection, patient, study, and series levels. The table also contains download-related attributes, such as the AWS S3 bucket and URL to download the series. |
| `analysis_results_index` | 26 | 0.0 MB | This table contains metadata about the analysis results collections available in IDC. Each row corresponds to an analysis results collection, and contains attributes such as the collection name, types of cancer represented, number of subjects, and pointers to the resources to learn more about the content of the collection |
| `ann_group_index` | 17,369 | 0.5 MB | This table contains detailed metadata about individual annotation groups within Microscopy Bulk Simple Annotations (ANN) series in IDC. Each row corresponds to a single annotation group, providing granular information about the graphic type, number of annotations, property codes, and algorithm details. This table can be joined with ann_index using SeriesInstanceUID for series-level context. Note: ANN series are assumed to contain a single instance. |
| `ann_index` | 7,448 | 0.3 MB | This table contains metadata about the Microscopy Bulk Simple Annotations (ANN) series available in IDC. Each row corresponds to a DICOM series containing annotations, and includes attributes such as the annotation coordinate type and references to the annotated image series. For detailed group-level information (counts, graphic types, property codes), join with ann_group_index using SeriesInstanceUID. This table can be joined with the main idc_index table using the SeriesInstanceUID column. Note: ANN series are assumed to contain a single instance. |
| `clinical_index` | 10,661 | 0.2 MB | This table contains metadata about the tabular data, including clinical data, accompanying images that is available in IDC. Think about this table as a dictionary containing information about the columns for all of the tabular data accompanying individual collections in IDC. Each row corresponds to a unique combination of collection, clinical data table that is available for that collection, and a column from that table. Individual tables referenced from this table can be retrieved using idc-index `get_clinical_table()` function. |
| `collections_index` | 179 | 0.1 MB | This table contains metadata about the collections available in IDC. Each row corresponds to a collection, and contains attributes such as the collection name, types of cancer represented, number of subjects, and pointers to the resources to learn more about the content of the collection. |
| `contrast_index` | 66,175 | 1.1 MB | This table contains one row per DICOM series that has contrast agent information. It captures contrast bolus metadata from CT, MR, PT, XA, and RF imaging modalities, including the agent name, ingredient, and administration route. Only series with at least one non-null contrast attribute are included. This table can be joined with the main idc_index table using the SeriesInstanceUID column. |
| `ct_index` | 269,895 | 6.5 MB | This table contains one row per CT Image Storage (SOPClassUID 1.2.840.10008.5.1.4.1.1.2) DICOM series in IDC, capturing acquisition and reconstruction parameters that are not included in the main idc_index table. The index can be joined to idc_index on SeriesInstanceUID to combine universal series metadata with CT-specific acquisition parameters. For XRayTubeCurrent, Exposure, and ExposureTime — which vary across instances within a series due to dose modulation — both min and max values are reported. All other attributes are aggregated with ANY_VALUE (one representative instance). |
| `mr_index` | 126,452 | 3.0 MB | This table contains one row per MR Image Storage (SOPClassUID 1.2.840.10008.5.1.4.1.1.4) DICOM series in IDC, capturing MR acquisition and sequence parameters that are not included in the main idc_index table. The index can be joined to idc_index on SeriesInstanceUID to combine universal series metadata with MR-specific acquisition parameters. EchoTime and DiffusionBValue are reported as arrays of all distinct per-instance values because they legitimately differ across instances within multi-echo and diffusion-weighted series respectively. All other attributes are aggregated with ANY_VALUE (one representative instance). |
| `pt_index` | 5,885 | 0.5 MB | This table contains one row per Positron Emission Tomography Image Storage (SOPClassUID 1.2.840.10008.5.1.4.1.1.128) DICOM series in IDC, capturing PET acquisition, reconstruction and radiopharmaceutical parameters that are not included in the main idc_index table. The index can be joined to idc_index on SeriesInstanceUID to combine universal series metadata with PET-specific acquisition parameters. ActualFrameDuration is reported as an array of all distinct per-instance values because it legitimately varies across frames in dynamic (multi-frame) PET acquisitions. All other attributes are constant within a series and are aggregated with ANY_VALUE. |
| `rtstruct_index` | 19,366 | 0.4 MB | This table contains one row per DICOM RT Structure Set (RTSTRUCT) SeriesInstanceUID available from IDC, and captures key metadata about the structure set including the number of ROIs, ROI names, generation algorithms, interpreted types, and the referenced image series. Note: multi-valued columns (ROINames, ROIGenerationAlgorithms, RTROIInterpretedTypes) are aggregated with DISTINCT independently, so positional correspondence between columns is not preserved. |
| `seg_index` | 193,154 | 6.6 MB | This table contains one row per DICOM Segmentation SeriesInstanceUID available from IDC, and captures key metadata about the segmentation series including the number of segments, segmentation type, algorithm type and name, and the segmented image series. Note: multi-valued columns (AlgorithmType, AlgorithmName, SegmentedPropertyCategory_CodeMeanings, etc.) are aggregated with DISTINCT independently, so positional correspondence between columns is not preserved. For example, the first value in AlgorithmType does not necessarily pair with the first value in AlgorithmName. |
| `sm_index` | 76,300 | 2.2 MB | This table contains metadata about the slide microscopy (SM) series available in IDC. Each row corresponds to a DICOM series, and contains attributes specific to SM series, such as the pixel spacing at the maximum resolution layer, the power of the objective lens used to digitize the slide, and the anatomic location from where the imaged specimen was collected. This table can be joined with the main index table using the `SeriesInstanceUID` column. |
| `sm_instance_index` | 391,939 | 17.9 MB | This table contains metadata about the slide microscopy (SM) series available in IDC. Each row corresponds to an instance from a DICOM Slide Microscopy series available from IDC, identified by `SOPInstanceUID`, and contains attributes specific to SM series, such as the pixel spacing at the maximum resolution layer, the power of the objective lens used to digitize the slide, and the anatomic location from where the imaged specimen was collected. This table can be joined with the main index table and/or with `sm_index` using the `SeriesInstanceUID` column. |
| `version_metadata_index` | 25 | 0.0 MB | This table contains metadata about each IDC data release version. Each row corresponds to one IDC version and captures when that version was created. This index can be used to correlate data in other indexes (which include idc_version columns) with the corresponding release timestamps. |
| `volume_geometry_index` | 326,467 | 5.2 MB | This table contains one row per DICOM series from IDC for single-frame CT, MR, and PT SOP classes, with boolean columns characterizing the geometric properties of each series. The checks determine whether the series forms a regularly-spaced rectilinear 3D volume (consistent orientation, spacing, dimensions, and slice positions). Series that do not pass all checks may still be usable with additional processing such as resampling or acquisition geometry correction (e.g., for variable-spacing or gantry-tilted acquisitions). Oblique-aware: uses projection-based slice position computation, which handles gantry-tilted CT, oblique MR, and axial PET uniformly. |

> [!NOTE]
> `clinical_index` is a _dictionary_ of the clinical tables and columns
> available per collection -- not the clinical data itself. The clinical tables
> are not among these artifacts; retrieve them with
> `IDCClient.get_clinical_table()`.

## Data fields

Columns of `idc_index`, the default config:

| Column | Type | Description |
|---|---|---|
| `collection_id` | STRING | short string with the identifier of the collection the series belongs to |
| `analysis_result_id` | STRING | this string is not empty if the specific series is part of an analysis results collection; analysis results can be added to a given collection over time |
| `PatientID` | STRING | identifier of the patient within the collection (DICOM attribute) |
| `SeriesInstanceUID` | STRING |  |
| `StudyInstanceUID` | STRING | unique identifier of the DICOM study (DICOM attribute) |
| `source_DOI` | STRING | Digital Object Identifier of the dataset that contains the given series; follow this DOI to learn more about the activity that produced this series |
| `PatientAge` | STRING | age of the subject at the time of imaging (DICOM attribute) |
| `PatientSex` | STRING | subject sex (DICOM attribute) |
| `StudyDate` | STRING | date of the study (de-identified) (DICOM attribute) |
| `StudyDescription` | STRING | textual description of the study content (DICOM attribute) |
| `BodyPartExamined` | STRING | body part imaged (not applicable for SM series) (DICOM attribute). For derived series (SEG, RTSTRUCT) this reflects the source acquisition, not the segmented anatomy -- what was segmented is recorded in the seg_index table (SegmentedPropertyType_CodeMeanings) and in rtstruct_index. |
| `Modality` | STRING | acquisition modality (DICOM attribute) |
| `SOPClassUID` | STRING | SOP Class UID identifying the type of DICOM object (e.g., CT Image Storage, Segmentation Storage); more specific than Modality for distinguishing object types (DICOM attribute) |
| `sop_class_name` | STRING | human-readable name of the SOP Class (e.g., "CT Image Storage", "Segmentation Storage"); derived from SOPClassUID |
| `TransferSyntaxUID` | STRING | Transfer Syntax UID identifying the encoding of the stored instances (e.g., Explicit VR Little Endian, JPEG 2000, HTJ2K); comma-separated when a series contains instances with different encodings, which is common for SM (DICOM attribute) |
| `transfer_syntax_name` | STRING | human-readable name of the Transfer Syntax (e.g., "JPEG 2000", "Explicit VR Little Endian"); comma-separated when a series contains instances with different encodings; derived from TransferSyntaxUID |
| `PhotometricInterpretation` | STRING | intended interpretation of the pixel data, e.g., MONOCHROME2 (grayscale), RGB or YBR_FULL_422 (color); NULL for non-image objects (e.g., SEG, SR, RTSTRUCT); comma-separated when a series contains instances with different values (DICOM attribute) |
| `PixelRepresentation` | STRING | data representation of pixel sample values: "0" for unsigned integers, "1" for signed integers; NULL for non-image objects; comma-separated when a series contains instances with different values (DICOM attribute) |
| `Manufacturer` | STRING | manufacturer of the equipment that produced the series (DICOM attribute) |
| `ManufacturerModelName` | STRING | model name of the equipment that produced the series (DICOM attribute) |
| `SeriesDate` | STRING | date of the series (de-identified) (DICOM attribute) |
| `SeriesDescription` | STRING | textual description of the series content (DICOM attribute) |
| `SeriesNumber` | STRING | series number (DICOM attribute) |
| `instanceCount` | INTEGER | number of instances in the series |
| `license_short_name` | STRING | short name of the license that applies to this series |
| `series_init_idc_version` | INTEGER | IDC data release version number when this series first appeared in IDC (integer, e.g., 1 for v1) |
| `series_revised_idc_version` | INTEGER | IDC data release version number when this series was most recently revised in IDC (integer, e.g., 24 for v24) |
| `aws_bucket` | STRING | name of the AWS S3 bucket that contains the series |
| `crdc_series_uuid` | STRING | unique identifier of the series within the IDC |
| `series_aws_url` | STRING | public AWS S3 URL to download the series in bulk (each instance is a separate file) |
| `series_size_MB` | FLOAT | total size of the series in megabytes |

Every other config is described by a `<config>_schema.json` sidecar in this
repository, carrying the same table and column descriptions:

| Config | Columns | Schema |
|---|---:|---|
| `analysis_results_index` | 14 | [`analysis_results_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/analysis_results_index_schema.json) |
| `ann_group_index` | 12 | [`ann_group_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/ann_group_index_schema.json) |
| `ann_index` | 3 | [`ann_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/ann_index_schema.json) |
| `clinical_index` | 6 | [`clinical_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/clinical_index_schema.json) |
| `collections_index` | 12 | [`collections_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/collections_index_schema.json) |
| `contrast_index` | 4 | [`contrast_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/contrast_index_schema.json) |
| `ct_index` | 21 | [`ct_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/ct_index_schema.json) |
| `mr_index` | 22 | [`mr_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/mr_index_schema.json) |
| `pt_index` | 21 | [`pt_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/pt_index_schema.json) |
| `rtstruct_index` | 6 | [`rtstruct_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/rtstruct_index_schema.json) |
| `seg_index` | 9 | [`seg_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/seg_index_schema.json) |
| `sm_index` | 20 | [`sm_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/sm_index_schema.json) |
| `sm_instance_index` | 15 | [`sm_instance_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/sm_instance_index_schema.json) |
| `version_metadata_index` | 2 | [`version_metadata_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/version_metadata_index_schema.json) |
| `volume_geometry_index` | 11 | [`volume_geometry_index_schema.json`](https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/blob/main/volume_geometry_index_schema.json) |

They are plain JSON, so you can read one without downloading the data:

```python
import json, urllib.request

url = "https://huggingface.co/datasets/ImagingDataCommons/idc-index-data/resolve/main/seg_index_schema.json"
schema = json.load(urllib.request.urlopen(url))
print(schema["table_description"])
for column in schema["columns"]:
    print(column["name"], "--", column.get("description", ""))
```

## Licensing

**The images are not covered by a single license.** Every row carries a
`license_short_name` giving the license of that series; the YAML above lists all
of them so the dataset appears under each one's Hub filter. Check it per series
before redistributing or using data commercially.

| License | Series | Commercial use |
|---|---:|---|
| CC BY 4.0 | 877,215 | allowed |
| CC BY 3.0 | 132,303 | allowed |
| CC BY-NC 4.0 | 28,783 | not allowed |
| CC BY-NC 3.0 | 5,851 | not allowed |
| National Library of Medicine Terms and Conditions; May 21, 2019 | 39 | see terms |

Series under _National Library of Medicine Terms and Conditions_ are governed by
<https://www.nlm.nih.gov/databases/download/terms_and_conditions.html>.

Every license IDC uses -- CC BY and CC BY-NC alike -- requires **attribution**.
See [IDC licensing and attribution](https://learn.canceridc.dev/data/licensing).

The index files in this repository are a factual catalog of that content and are
distributed under the license of the
[idc-index-data repository](https://github.com/ImagingDataCommons/idc-index-data/blob/main/LICENSE). That license
covers the tables only, never the referenced images.

## Attribution and citation

Attribution is required by every license in this catalog, and it is owed to the
**source dataset**, not to IDC. Each row's `source_DOI` identifies the dataset
the series came from; resolve it to a formatted citation with IDC's citations
API or `IDCClient.citations_from_selection()`.

Many IDC collections originate from
[The Cancer Imaging Archive (TCIA)](https://www.cancerimagingarchive.net/); IDC
is a TCIA Data Analysis Center. Those collections additionally carry TCIA's
[data usage policies and restrictions](https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/),
including obligations on downstream attribution.

Please also acknowledge IDC itself:

```bibtex
@article{fedorov2023idc,
  title   = {National Cancer Institute Imaging Data Commons: Toward Transparency,
             Reproducibility, and Scalability in Imaging Artificial Intelligence},
  author  = {Fedorov, Andrey and Longabaugh, William J. R. and Pot, David and
             Clunie, David A. and Pieper, Steven D. and Gibbs, David L. and
             Bridge, Christopher and Herrmann, Markus D. and Homeyer, Andr\'e and
             Lewis, Rob and Aerts, Hugo J. W. L. and Krishnaswamy, Deepa and
             Thiriveedhi, Vamsi K. and Ciausu, Cosmin and Schacherer, David P. and
             Bontempi, Dennis and Pihl, Todd and Wagner, Ulrike and
             Farahani, Keyvan and Kim, Erika and Kikinis, Ron},
  journal = {RadioGraphics},
  volume  = {43},
  number  = {12},
  year    = {2023},
  doi     = {10.1148/rg.230180}
}
```

## Versioning

Tags on this repo match the [idc-index-data releases](https://github.com/ImagingDataCommons/idc-index-data/releases)
one for one, and `main` always holds the most recent published release. Pin a
version to keep results reproducible:

```python
load_dataset("ImagingDataCommons/idc-index-data", "idc_index", revision="25.0.0")
```

This release, `25.0.0`, indexes IDC v25 (released 2026-09-26). Not every idc-index-data
release is published here; tags on this repo are a subset of the GitHub
releases.

This card is generated, not maintained here. Every publish regenerates
`README.md` -- YAML front matter and all -- from the release artifacts and
commits it over whatever the Hub currently holds. Edits made through the Hub UI
and community pull requests merged into this card are reverted by the next
publish, with no warning and no notification to whoever made them. The old text
survives only in this repo's commit history.

So please don't send card fixes as pull requests here; they will not last. Open
them against the template the wording comes from,
[`scripts/hf/card_template.md`](https://github.com/ImagingDataCommons/idc-index-data/blob/main/scripts/hf/card_template.md),
and they will appear at the next release. Nothing else on the Hub is affected:
discussions persist, and only the Parquet files, their `*_schema.json` sidecars
and this card are ever written or removed by the publishing job.

## Links

- [IDC portal](https://portal.imaging.datacommons.cancer.gov/explore/) -- browse the data and build cohorts
  interactively
- [Visualizing IDC images](https://learn.canceridc.dev/portal/visualization) -- how the browser viewers
  work; get a link to any series with `IDCClient.get_viewer_URL()`
- [IDC agent interfaces](https://learn.canceridc.dev/ai-assistants/agents) -- search IDC, size a cohort and get a
  download command by asking: hosted MCP server, agent skill, or REST API
- [IDC documentation](https://learn.canceridc.dev/)
- [`idc-index` Python package](https://github.com/ImagingDataCommons/idc-index)
  -- the download client (`pip install idc-index`)
- [`idc-index-data` on GitHub](https://github.com/ImagingDataCommons/idc-index-data) -- how these tables are built
  (SQL included)
- [`highdicom` Python package](https://github.com/ImagingDataCommons/highdicom)
  -- used to read DICOM and arrange as tensors
- [GCS mirror of the release artifacts](https://storage.googleapis.com/idc-index-data-artifacts?prefix=current/release_artifacts/)
  -- fetch a single file directly, e.g.
  `https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/idc_index.parquet`
- [IDC user forum](https://discourse.canceridc.dev/)