imaging-data-commons

Imported from mkurman/zorai (skills/scientific-skills/imaging-data-commons/SKILL.md). Install upstream with npx skills add mkurman/zorai --skill imaging-data-commons. Copyright stays with the author (This skill is provided under the MIT Lic).

-|--------------| | index_tables_guide.md | Complex JOINs, schema discovery, DataFrame access | | use_cases.md | End-to-end workflow examples (training datasets, batch downloads) | | sql_patterns.md | Quick SQL patterns for filter discovery, annotations, size estimation | | clinical_data_guide.md | Clinical/tabular data, imaging+clinical joins, value mapping | | cloud_storage_guide.md | Direct S3/GCS access, versioning, UUID mapping | | dicomweb_guide.md | DICOMweb endpoints, PACS integration | | digital_pathology_guide.md | Slide microscopy (SM), annotations (ANN), pathology workflows | | bigquery_guide.md | Full DICOM metadata, private elements (requires GCP) | | cli_guide.md | Command-line tools (idc download, manifest files) |

IDC Data Model

IDC adds two grouping levels above the standard DICOM hierarchy (Patient → Study → Series → Instance):

• collection_id: Groups patients by disease, modality, or research focus (e.g., tcga_luad, nlst). A patient belongs to exactly one collection.
• analysis_result_id: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections.

Use collection_id to find original imaging data, may include annotations deposited along with the images; use analysis_result_id to find AI-generated or expert annotations.

Key identifiers for queries:

Index Tables

The idc-index package provides multiple metadata index tables, accessible via SQL or as pandas DataFrames.

Complete index table documentation: Use https://idc-index.readthedocs.io/en/latest/indices_reference.html for quick check of available tables and columns without executing any code.

Important: Use client.indices_overview to get current table descriptions and column schemas. This is the authoritative source for available columns and their types — always query it when writing SQL or exploring data structure.

Available Tables

Auto = loaded automatically when IDCClient() is instantiated fetch_index() = requires client.fetch_index("table_name") to load

Joining Tables

Key columns are not explicitly labeled, the following is a subset that can be used in joins.

Note: Subjects, Updated, and Description appear in multiple tables but have different meanings (counts vs identifiers, different update contexts).

For detailed join examples, schema discovery patterns, key columns reference, and DataFrame access, see references/index_tables_guide.md.

Clinical Data Access

# Fetch clinical index (also downloads clinical data tables)
client.fetch_index("clinical_index")

# Query clinical index to find available tables and their columns
tables = client.sql_query("SELECT DISTINCT table_name, column_label FROM clinical_index")

# Load a specific clinical table as DataFrame
clinical_df = client.get_clinical_table("table_name")

See references/clinical_data_guide.md for detailed workflows including value mapping patterns and joining clinical data with imaging.

Data Access Options

Cloud storage organization

IDC maintains all DICOM files in public cloud storage buckets mirrored between AWS S3 and Google Cloud Storage. Files are organized by CRDC UUIDs (not DICOM UIDs) to support versioning.

Files are stored as <crdc_series_uuid>/<crdc_instance_uuid>.dcm. Access is free (no egress fees) via AWS CLI, gsutil, or s5cmd with anonymous access. Use series_aws_url column from the index for S3 URLs; GCS uses the same path structure.

See references/cloud_storage_guide.md for bucket details, access commands, UUID mapping, and versioning.

DICOMweb access

IDC data is available via DICOMweb interface (Google Cloud Healthcare API implementation) for integration with PACS systems and DICOMweb-compatible tools.

See references/dicomweb_guide.md for endpoint URLs, code examples, supported operations, and implementation details.

Installation and Setup

Required (for basic access):

pip install --upgrade idc-index

Important: New IDC data release will always trigger a new version of idc-index. Always use --upgrade flag while installing, unless an older version is needed for reproducibility.

IMPORTANT: IDC data version v23 is current. Always verify your version:

print(client.get_idc_version())  # Should return "v23"

If you see an older version, upgrade with: pip install --upgrade idc-index

Tested with: idc-index 0.11.10 (IDC data version v23)

Optional (for data analysis):

pip install pandas numpy pydicom

Core Capabilities

1. Data Discovery and Exploration

Discover what imaging collections and data are available in IDC:

from idc_index import IDCClient

client = IDCClient()

# Get summary statistics from primary index
query = """
SELECT
  collection_id,
  COUNT(DISTINCT PatientID) as patients,
  COUNT(DISTINCT SeriesInstanceUID) as series,
  SUM(series_size_MB) as size_mb
FROM index
GROUP BY collection_id
ORDER BY patients DESC
"""
collections_summary = client.sql_query(query)

# For richer collection metadata, use collections_index
client.fetch_index("collections_index")
collections_info = client.sql_query("""
    SELECT collection_id, CancerTypes, TumorLocations, Species, Subjects, SupportingData
    FROM collections_index
""")

# For analysis results (annotations, segmentations), use analysis_results_index
client.fetch_index("analysis_results_index")
analysis_info = client.sql_query("""
    SELECT analysis_result_id, analysis_result_title, Subjects, Collections, Modalities
    FROM analysis_results_index
""")

collections_index provides curated metadata per collection: cancer types, tumor locations, species, subject counts, and supporting data types — without needing to aggregate from the primary index.

analysis_results_index lists derived datasets (AI segmentations, expert annotations, radiomics features) with their source collections and modalities.

2. Querying Metadata with SQL

Query the IDC mini-index using SQL to find specific datasets.

First, explore available values for filter columns:

from idc_index import IDCClient

client = IDCClient()

# Check what Modality values exist
modalities = client.sql_query("""
    SELECT DISTINCT Modality, COUNT(*) as series_count
    FROM index
    GROUP BY Modality
    ORDER BY series_count DESC
""")
print(modalities)

# Check what BodyPartExamined values exist for MR modality
body_parts = client.sql_query("""
    SELECT DISTINCT BodyPartExamined, COUNT(*) as series_count
    FROM index
    WHERE Modality = 'MR' AND BodyPartExamined IS NOT NULL
    GROUP BY BodyPartExamined
    ORDER BY series_count DESC
    LIMIT 20
""")
print(body_parts)

Then query with validated filter values:

# Find breast MRI scans (use actual values from exploration above)
results = client.sql_query("""
    SELECT
      collection_id,
      PatientID,
      SeriesInstanceUID,
      Modality,
      SeriesDescription,
      license_short_name
    FROM index
    WHERE Modality = 'MR'
      AND BodyPartExamined = 'BREAST'
    LIMIT 20
""")

# Access results as pandas DataFrame
for idx, row in results.iterrows():
    print(f"Patient: {row['PatientID']}, Series: {row['SeriesInstanceUID']}")

To filter by cancer type, join with collections_index:

client.fetch_index("collections_index")
results = client.sql_query("""
    SELECT i.collection_id, i.PatientID, i.SeriesInstanceUID, i.Modality
    FROM index i
    JOIN collections_index c ON i.collection_id = c.collection_id
    WHERE c.CancerTypes LIKE '%Breast%'
      AND i.Modality = 'MR'
    LIMIT 20
""")

Available metadata fields (use client.indices_overview for complete list):

• Identifiers: collection_id, PatientID, StudyInstanceUID, SeriesInstanceUID
• Imaging: Modality, BodyPartExamined, Manufacturer, ManufacturerModelName
• Clinical: PatientAge, PatientSex, StudyDate
• Descriptions: StudyDescription, SeriesDescription
• Licensing: license_short_name

Note: Cancer type is in collections_index.CancerTypes, not in the primary index table.

3. Downloading DICOM Files

Download imaging data efficiently from IDC's cloud storage:

Download entire collection:

from idc_index import IDCClient

client = IDCClient()

# Download small collection (RIDER Pilot ~1GB)
client.download_from_selection(
    collection_id="rider_pilot",
    downloadDir="./data/rider"
)

Download specific series:

# First, query for series UIDs
series_df = client.sql_query("""
    SELECT SeriesInstanceUID
    FROM index
    WHERE Modality = 'CT'
      AND BodyPartExamined = 'CHEST'
      AND collection_id = 'nlst'
    LIMIT 5
""")

# Download only those series
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/lung_ct"
)

Custom directory structure:

Default dirTemplate: %collection_id/%PatientID/%StudyInstanceUID/%Modality_%SeriesInstanceUID

# Simplified hierarchy (omit StudyInstanceUID level)
client.download_from_selection(
    collection_id="tcga_luad",
    downloadDir="./data",
    dirTemplate="%collection_id/%PatientID/%Modality"
)
# Results in: ./data/tcga_luad/TCGA-05-4244/CT/

# Flat structure (all files in one directory)
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/flat",
    dirTemplate=""
)
# Results in: ./data/flat/*.dcm

Downloaded file names:

Individual DICOM files are named using their CRDC instance UUID: <crdc_instance_uuid>.dcm (e.g., 0d73f84e-70ae-4eeb-96a0-1c613b5d9229.dcm). This UUID-based naming:

• Enables version tracking (UUIDs change when file content changes)
• Matches cloud storage organization (s3://idc-open-data/<crdc_series_uuid>/<crdc_instance_uuid>.dcm)
• Differs from DICOM UIDs (SOPInstanceUID) which are preserved inside the file metadata

To identify files, use the crdc_instance_uuid column in queries or read DICOM metadata (SOPInstanceUID) from the files.

Command-Line Download

The idc download command provides command-line access to download functionality without writing Python code. Available after installing idc-index.

Auto-detects input type: manifest file path, or identifiers (collection_id, PatientID, StudyInstanceUID, SeriesInstanceUID, crdc_series_uuid).

# Download entire collection
idc download rider_pilot --download-dir ./data

# Download specific series by UID
idc download "1.3.6.1.4.1.9328.50.1.69736" --download-dir ./data

# Download multiple items (comma-separated)
idc download "tcga_luad,tcga_lusc" --download-dir ./data

# Download from manifest file (auto-detected)
idc download manifest.txt --download-dir ./data

Options:

Manifest files:

Manifest files contain S3 URLs (one per line) and can be:

• Exported from the IDC Portal after cohort selection
• Shared by collaborators for reproducible data access
• Generated programmatically from query results

Format (one S3 URL per line):

s3://idc-open-data/cb09464a-c5cc-4428-9339-d7fa87cfe837/*
s3://idc-open-data/88f3990d-bdef-49cd-9b2b-4787767240f2/*

Example: Generate manifest from Python query:

from idc_index import IDCClient

client = IDCClient()

# Query for series URLs
results = client.sql_query("""
    SELECT series_aws_url
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
""")

# Save as manifest file
with open('ct_manifest.txt', 'w') as f:
    for url in results['series_aws_url']:
        f.write(url + '\n')

Then download:

idc download ct_manifest.txt --download-dir ./ct_data

4. Visualizing IDC Images

View DICOM data in browser without downloading:

from idc_index import IDCClient
import webbrowser

client = IDCClient()

# First query to get valid UIDs
results = client.sql_query("""
    SELECT SeriesInstanceUID, StudyInstanceUID
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
    LIMIT 1
""")

# View single series
viewer_url = client.get_viewer_URL(seriesInstanceUID=results.iloc[0]['SeriesInstanceUID'])
webbrowser.open(viewer_url)

# View all series in a study (useful for multi-series exams like MRI protocols)
viewer_url = client.get_viewer_URL(studyInstanceUID=results.iloc[0]['StudyInstanceUID'])
webbrowser.open(viewer_url)

The method automatically selects OHIF v3 for radiology or SLIM for slide microscopy. Viewing by study is useful when a DICOM Study contains multiple Series (e.g., T1, T2, DWI sequences from a single MRI session).

5. Understanding and Checking Licenses

Check data licensing before use (critical for commercial applications):

from idc_index import IDCClient

client = IDCClient()

# Check licenses for all collections
query = """
SELECT DISTINCT
  collection_id,
  license_short_name,
  COUNT(DISTINCT SeriesInstanceUID) as series_count
FROM index
GROUP BY collection_id, license_short_name
ORDER BY collection_id
"""

licenses = client.sql_query(query)
print(licenses)

License types in IDC:

• CC BY 4.0 / CC BY 3.0 (~97% of data) - Allows commercial use with attribution
• CC BY-NC 4.0 / CC BY-NC 3.0 (~3% of data) - Non-commercial use only
• Custom licenses (rare) - Some collections have specific terms (e.g., NLM Terms and Conditions)

Important: Always check the license before using IDC data in publications or commercial applications. Each DICOM file is tagged with its specific license in metadata.

Generating Citations for Attribution

The source_DOI column contains DOIs linking to publications describing how the data was generated. To satisfy attribution requirements, use citations_from_selection() to generate properly formatted citations:

from idc_index import IDCClient

client = IDCClient()

# Get citations for a collection (APA format by default)
citations = client.citations_from_selection(collection_id="rider_pilot")
for citation in citations:
    print(citation)

# Get citations for specific series
results = client.sql_query("""
    SELECT SeriesInstanceUID FROM index
    WHERE collection_id = 'tcga_luad' LIMIT 5
""")
citations = client.citations_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values)
)

# Alternative format: BibTeX (for LaTeX documents)
bibtex_citations = client.citations_from_selection(
    collection_id="tcga_luad",
    citation_format=IDCClient.CITATION_FORMAT_BIBTEX
)

Parameters:

• collection_id: Filter by collection(s)
• patientId: Filter by patient ID(s)
• studyInstanceUID: Filter by study UID(s)
• seriesInstanceUID: Filter by series UID(s)
• citation_format: Use IDCClient.CITATION_FORMAT_* constants:
  • CITATION_FORMAT_APA (default) - APA style
  • CITATION_FORMAT_BIBTEX - BibTeX for LaTeX
  • CITATION_FORMAT_JSON - CSL JSON
  • CITATION_FORMAT_TURTLE - RDF Turtle

Best practice: When publishing results using IDC data, include the generated citations to properly attribute the data sources and satisfy license requirements.

6. Batch Processing and Filtering

Process large datasets efficiently with filtering:

from idc_index import IDCClient
import pandas as pd

client = IDCClient()

# Find chest CT scans from GE scanners
query = """
SELECT
  SeriesInstanceUID,
  PatientID,
  collection_id,
  ManufacturerModelName
FROM index
WHERE Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
  AND Manufacturer = 'GE MEDICAL SYSTEMS'
  AND license_short_name = 'CC BY 4.0'
LIMIT 100
"""

results = client.sql_query(query)

# Save manifest for later
results.to_csv('lung_ct_manifest.csv', index=False)

# Download in batches to avoid timeout
batch_size = 10
for i in range(0, len(results), batch_size):
    batch = results.iloc[i:i+batch_size]
    client.download_from_selection(
        seriesInstanceUID=list(batch['SeriesInstanceUID'].values),
        downloadDir=f"./data/batch_{i//batch_size}"
    )

7. Advanced Queries with BigQuery

For queries requiring full DICOM metadata, complex JOINs, clinical data tables, or private DICOM elements, use Google BigQuery. Requires GCP account with billing enabled.

Quick reference:

• Dataset: bigquery-public-data.idc_current.*
• Main table: dicom_all (combined metadata)
• Full metadata: dicom_metadata (all DICOM tags)
• Private elements: OtherElements column (vendor-specific tags like diffusion b-values)

See references/bigquery_guide.md for setup, table schemas, query patterns, private element access, and cost optimization.

Before using BigQuery, always check if a specialized index table already has the metadata you need:

1. Use client.indices_overview or the idc-index indices reference to discover all available tables and their columns
2. Fetch the relevant index: client.fetch_index("table_name")
3. Query locally with client.sql_query() (free, no GCP account needed)

Common specialized indices: seg_index (segmentations), ann_index / ann_group_index (microscopy annotations), sm_index (slide microscopy), collections_index (collection metadata). Only use BigQuery if you need private DICOM elements or attributes not in any index.

8. Tool Selection Guide

Default choice: Use idc-index for most tasks (no auth, easy API, batch downloads).

9. Integration with Analysis Pipelines

Integrate IDC data into imaging analysis workflows:

Read downloaded DICOM files:

import pydicom
import os

# Read DICOM files from downloaded series
series_dir = "./data/rider/rider_pilot/RIDER-1007893286/CT_1.3.6.1..."

dicom_files = [os.path.join(series_dir, f) for f in os.listdir(series_dir)
               if f.endswith('.dcm')]

# Load first image
ds = pydicom.dcmread(dicom_files[0])
print(f"Patient ID: {ds.PatientID}")
print(f"Modality: {ds.Modality}")
print(f"Image shape: {ds.pixel_array.shape}")

Build 3D volume from CT series:

import pydicom
import numpy as np
from pathlib import Path

def load_ct_series(series_path):
    """Load CT series as 3D numpy array"""
    files = sorted(Path(series_path).glob('*.dcm'))
    slices = [pydicom.dcmread(str(f)) for f in files]

    # Sort by slice location
    slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))

    # Stack into 3D array
    volume = np.stack([s.pixel_array for s in slices])

    return volume, slices[0]  # Return volume and first slice for metadata

volume, metadata = load_ct_series("./data/lung_ct/series_dir")
print(f"Volume shape: {volume.shape}")  # (z, y, x)

Integrate with SimpleITK:

import SimpleITK as sitk
from pathlib import Path

# Read DICOM series
series_path = "./data/ct_series"
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(series_path)
reader.SetFileNames(dicom_names)
image = reader.Execute()

# Apply processing
smoothed = sitk.CurvatureFlow(image1=image, timeStep=0.125, numberOfIterations=5)

# Save as NIfTI
sitk.WriteImage(smoothed, "processed_volume.nii.gz")

Common Use Cases

See references/use_cases.md for complete end-to-end workflow examples including:

• Building deep learning training datasets from lung CT scans
• Comparing image quality across scanner manufacturers
• Previewing data in browser before downloading
• License-aware batch downloads for commercial use

Best Practices

• Verify IDC version before generating responses - Always call client.get_idc_version() at the start of a session to confirm you're using the expected data version (currently v23). If using an older version, recommend pip install --upgrade idc-index
• Check licenses before use - Always query the license_short_name field and respect licensing terms (CC BY vs CC BY-NC)
• Generate citations for attribution - Use citations_from_selection() to get properly formatted citations from source_DOI values; include these in publications
• Start with small queries - Use LIMIT clause when exploring to avoid long downloads and understand data structure
• Use mini-index for simple queries - Only use BigQuery when you need comprehensive metadata or complex JOINs
• Organize downloads with dirTemplate - Use meaningful directory structures like %collection_id/%PatientID/%Modality
• Cache query results - Save DataFrames to CSV files to avoid re-querying and ensure reproducibility
• Estimate size first - Check collection size before downloading - some collection sizes are in terabytes!
• Save manifests - Always save query results with Series UIDs for reproducibility and data provenance
• Read documentation - IDC data structure and metadata fields are documented at https://learn.canceridc.dev/
• Use IDC forum - Search for questons/answers and ask your questions to the IDC maintainers and users at https://discourse.canceridc.dev/

Troubleshooting

Issue: ModuleNotFoundError: No module named 'idc_index'

• Cause: idc-index package not installed
• Solution: Install with pip install --upgrade idc-index

Issue: Download fails with connection timeout

• Cause: Network instability or large download size
• Solution:
  • Download smaller batches (e.g., 10-20 series at a time)
  • Check network connection
  • Use dirTemplate to organize downloads by batch
  • Implement retry logic with delays

Issue: BigQuery quota exceeded or billing errors

• Cause: BigQuery requires billing-enabled GCP project
• Solution: Use idc-index mini-index for simple queries (no billing required), or see references/bigquery_guide.md for cost optimization tips

Issue: Series UID not found or no data returned

• Cause: Typo in UID, data not in current IDC version, or wrong field name
• Solution:
  • Check if data is in current IDC version (some old data may be deprecated)
  • Use LIMIT 5 to test query first
  • Check field names against metadata schema documentation

Issue: Downloaded DICOM files won't open

• Cause: Corrupted download or incompatible viewer
• Solution:
  • Check DICOM object type (Modality and SOPClassUID attributes) - some object types require specialized tools
  • Verify file integrity (check file sizes)
  • Use pydicom to validate: pydicom.dcmread(file, force=True)
  • Try different DICOM viewer (3D Slicer, Horos, RadiAnt, QuPath)
  • Re-download the series

Common SQL Query Patterns

See references/sql_patterns.md for quick-reference SQL patterns including:

• Filter value discovery (modalities, body parts, manufacturers)
• Annotation and segmentation queries (including seg_index, ann_index joins)
• Slide microscopy queries (sm_index patterns)
• Download size estimation
• Clinical data linking

For segmentation and annotation details, also see references/digital_pathology_guide.md.

Related Skills

The following skills complement IDC workflows for downstream analysis and visualization:

DICOM Processing

• pydicom - Read, write, and manipulate downloaded DICOM files. Use for extracting pixel data, reading metadata, anonymization, and format conversion. Essential for working with IDC radiology data (CT, MR, PET).

Pathology and Slide Microscopy

See references/digital_pathology_guide.md for DICOM-compatible tools (highdicom, wsidicom, TIA-Toolbox, Slim viewer).

Metadata Visualization

• matplotlib - Low-level plotting for full customization. Use for creating static figures summarizing IDC query results (bar charts of modalities, histograms of series counts, etc.).
• seaborn - Statistical visualization with pandas integration. Use for quick exploration of IDC metadata distributions, relationships between variables, and categorical comparisons with attractive defaults.
• plotly - Interactive visualization. Use when you need hover info, zoom, and pan for exploring IDC metadata, or for creating web-embeddable dashboards of collection statistics.

Data Exploration

• exploratory-data-analysis - Comprehensive EDA on scientific data files. Use after downloading IDC data to understand file structure, quality, and characteristics before analysis.

Resources

Schema Reference (Primary Source)

Always use client.indices_overview for current column schemas. This ensures accuracy with the installed idc-index version:

# Get all column names and types for any table
schema = client.indices_overview["index"]["schema"]
columns = [(c['name'], c['type'], c.get('description', '')) for c in schema['columns']]

Reference Documentation

See the Quick Navigation section at the top for the full list of reference guides with decision triggers.

• indices_reference - External documentation for index tables (may be ahead of the installed version)

External Links

• IDC Portal: https://portal.imaging.datacommons.cancer.gov/explore/
• Documentation: https://learn.canceridc.dev/
• Tutorials: https://github.com/ImagingDataCommons/IDC-Tutorials
• User Forum: https://discourse.canceridc.dev/
• idc-index GitHub: https://github.com/ImagingDataCommons/idc-index
• Citation: Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180

Skill Updates

This skill version is available in skill metadata. To check for updates:

• Visit the releases page
• Watch the repository on GitHub (Watch → Custom → Releases)