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Digital Pathology Guide for IDC

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Digital Pathology Guide for IDC

Tested with: IDC data version v23, idc-index 0.11.10

For general IDC queries and downloads, use idc-index (see main SKILL.md). This guide covers slide microscopy (SM) imaging, microscopy bulk simple annotations (ANN), and segmentations (SEG) in the context of digital pathology in IDC.

Index Tables for Digital Pathology

Five specialized index tables provide curated metadata without needing BigQuery:

TableRow GranularityDescription
sm_index1 row = 1 SM seriesSlide Microscopy series metadata: container/slide ID, tissue type, anatomic structure, diagnosis, lens power, pixel spacing, image dimensions
sm_instance_index1 row = 1 SM instanceInstance-level (SOPInstanceUID) metadata for individual slide images
seg_index1 row = 1 SEG seriesDICOM Segmentation metadata: algorithm, segment count, reference to source series. Used for both radiology and pathology — filter by source Modality to find pathology-specific segmentations
ann_index1 row = 1 ANN seriesMicroscopy Bulk Simple Annotations series metadata; includes referenced_SeriesInstanceUID linking to the annotated slide
ann_group_index1 row = 1 annotation groupAnnotation group details: AnnotationGroupLabel, GraphicType, NumberOfAnnotations, AlgorithmName, property codes

All require client.fetch_index("table_name") before querying. Use client.indices_overview to inspect column schemas programmatically.

Slide Microscopy Queries

Basic SM metadata

python
from idc_index import IDCClient
client = IDCClient()

# sm_index has detailed metadata; join with index for collection_id
client.fetch_index("sm_index")
client.sql_query("""
    SELECT i.collection_id, COUNT(*) as slides,
           MIN(s.min_PixelSpacing_2sf) as min_resolution
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    GROUP BY i.collection_id
    ORDER BY slides DESC
""")

Find SM series with specific properties

python
# Find high-resolution slides with specific objective lens power
client.fetch_index("sm_index")
client.sql_query("""
    SELECT
        i.collection_id,
        i.PatientID,
        s.ObjectiveLensPower,
        s.min_PixelSpacing_2sf
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE s.ObjectiveLensPower >= 40
    ORDER BY s.min_PixelSpacing_2sf
    LIMIT 20
""")

Filter by specimen preparation

The sm_index includes staining, embedding, and fixative metadata. These columns are arrays (e.g., [hematoxylin stain, water soluble eosin stain] for H&E) — use array_to_string() with LIKE or list_contains() to filter.

python
# Find H&E-stained slides in a collection
client.fetch_index("sm_index")
client.sql_query("""
    SELECT
        i.PatientID,
        s.staining_usingSubstance_CodeMeaning as staining,
        s.embeddingMedium_CodeMeaning as embedding,
        s.tissueFixative_CodeMeaning as fixative
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'tcga_brca'
      AND array_to_string(s.staining_usingSubstance_CodeMeaning, ', ') LIKE '%hematoxylin%'
    LIMIT 10
""")
python
# Compare FFPE vs frozen slides across collections
client.sql_query("""
    SELECT
        i.collection_id,
        s.embeddingMedium_CodeMeaning as embedding,
        COUNT(*) as slide_count
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    GROUP BY i.collection_id, embedding
    ORDER BY i.collection_id, slide_count DESC
""")

Identifying Tumor vs Normal Slides

The sm_index table provides two ways to identify tissue type:

ColumnUse Case
primaryAnatomicStructureModifier_CodeMeaningStructured tissue type from DICOM specimen metadata (e.g., Neoplasm, Primary, Normal, Tumor, Neoplasm, Metastatic). Works across all collections with SM data.
ContainerIdentifierSlide/container identifier. For TCGA collections, contains the TCGA barcode where the sample type code (positions 14-15) encodes tissue origin: 01-09 = tumor, 10-19 = normal.

Using structured tissue type metadata

python
from idc_index import IDCClient
client = IDCClient()
client.fetch_index("sm_index")

# Discover tissue type values across all SM data
client.sql_query("""
    SELECT
        s.primaryAnatomicStructureModifier_CodeMeaning as tissue_type,
        COUNT(*) as slide_count
    FROM sm_index s
    WHERE s.primaryAnatomicStructureModifier_CodeMeaning IS NOT NULL
    GROUP BY tissue_type
    ORDER BY slide_count DESC
""")

Example: Tumor vs normal slides in TCGA-BRCA

python
# Tissue type breakdown for TCGA-BRCA
client.sql_query("""
    SELECT
        s.primaryAnatomicStructureModifier_CodeMeaning as tissue_type,
        COUNT(*) as slide_count,
        COUNT(DISTINCT i.PatientID) as patient_count
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'tcga_brca'
    GROUP BY tissue_type
    ORDER BY slide_count DESC
""")
# Returns: Neoplasm, Primary (2704 slides), Normal (399 slides)

Using TCGA barcode (TCGA collections only)

For TCGA collections, ContainerIdentifier contains the slide barcode (e.g., TCGA-E9-A3X8-01A-03-TSC). Extract the sample type code to classify tissue:

python
# Parse sample type from TCGA barcode
client.sql_query("""
    SELECT
        SUBSTRING(SPLIT_PART(s.ContainerIdentifier, '-', 4), 1, 2) as sample_type_code,
        s.primaryAnatomicStructureModifier_CodeMeaning as tissue_type,
        COUNT(*) as slide_count
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'tcga_brca'
    GROUP BY sample_type_code, tissue_type
    ORDER BY sample_type_code
""")
# Returns: 01 → Neoplasm, Primary (2704), 06 → None (8), 11 → Normal (399)

The barcode approach catches cases where structured metadata is NULL (e.g., 06 = Metastatic slides have primaryAnatomicStructureModifier_CodeMeaning = NULL in TCGA-BRCA).

Annotation Queries (ANN)

DICOM Microscopy Bulk Simple Annotations (Modality = 'ANN') are annotations on slide microscopy images. They appear in ann_index (series-level) and ann_group_index (group-level detail). Each ANN series references the slide it annotates via referenced_SeriesInstanceUID.

Basic annotation discovery

python
# Find annotation series and their referenced images
client.fetch_index("ann_index")
client.fetch_index("ann_group_index")

client.sql_query("""
    SELECT
        a.SeriesInstanceUID as ann_series,
        a.AnnotationCoordinateType,
        a.referenced_SeriesInstanceUID as source_series
    FROM ann_index a
    LIMIT 10
""")

Annotation group statistics

python
# Get annotation group details (graphic types, counts, algorithms)
client.sql_query("""
    SELECT
        GraphicType,
        SUM(NumberOfAnnotations) as total_annotations,
        COUNT(*) as group_count
    FROM ann_group_index
    GROUP BY GraphicType
    ORDER BY total_annotations DESC
""")

Find annotations with source slide context

python
# Find annotations with their source slide microscopy context
client.sql_query("""
    SELECT
        i.collection_id,
        g.GraphicType,
        g.AnnotationPropertyType_CodeMeaning,
        g.AlgorithmName,
        g.NumberOfAnnotations
    FROM ann_group_index g
    JOIN ann_index a ON g.SeriesInstanceUID = a.SeriesInstanceUID
    JOIN index i ON a.referenced_SeriesInstanceUID = i.SeriesInstanceUID
    WHERE g.AlgorithmName IS NOT NULL
    LIMIT 10
""")

Segmentations on Slide Microscopy

DICOM Segmentations (Modality = 'SEG') are used for both radiology (e.g., organ segmentations on CT) and pathology (e.g., tissue region segmentations on whole slide images). Use seg_index.segmented_SeriesInstanceUID to find the source series, then filter by source Modality to isolate pathology segmentations.

python
# Find segmentations whose source is a slide microscopy image
client.fetch_index("seg_index")
client.fetch_index("sm_index")
client.sql_query("""
    SELECT
        seg.SeriesInstanceUID as seg_series,
        seg.AlgorithmName,
        seg.total_segments,
        src.collection_id,
        src.Modality as source_modality
    FROM seg_index seg
    JOIN index src ON seg.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE src.Modality = 'SM'
    LIMIT 20
""")

Finding Pre-Computed Analysis Results

IDC hosts derived datasets (nuclei segmentations, TIL maps, AI annotations) identified by analysis_result_id in the main index table. Use analysis_results_index to discover what's available for pathology.

python
from idc_index import IDCClient
client = IDCClient()
client.fetch_index("analysis_results_index")

# Find analysis results that include pathology annotations or segmentations
client.sql_query("""
    SELECT
        ar.analysis_result_id,
        ar.analysis_result_title,
        ar.Modalities,
        ar.Subjects,
        ar.Collections
    FROM analysis_results_index ar
    WHERE ar.Modalities LIKE '%ANN%' OR ar.Modalities LIKE '%SEG%'
    ORDER BY ar.Subjects DESC
""")

Find analysis results for a specific slide

python
# Find all derived data (annotations, segmentations) for TCGA-BRCA slides
client.fetch_index("ann_index")
client.sql_query("""
    SELECT
        i.analysis_result_id,
        i.PatientID,
        a.referenced_SeriesInstanceUID as source_slide,
        g.AnnotationGroupLabel,
        g.NumberOfAnnotations,
        g.AlgorithmName
    FROM ann_group_index g
    JOIN ann_index a ON g.SeriesInstanceUID = a.SeriesInstanceUID
    JOIN index i ON a.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'tcga_brca'
    LIMIT 10
""")

Annotation objects can also contain per-annotation measurements (e.g., nucleus area, eccentricity) stored within the DICOM file. These are not in the index tables — extract them after download using highdicom (ann.get_annotation_groups(), group.get_measurements()). See the microscopy_dicom_ann_intro tutorial for a worked example including spatial analysis and cellularity computation.

Filter by AnnotationGroupLabel

AnnotationGroupLabel is the most direct column for finding annotation groups by name or semantic content. Use LIKE with wildcards for text search.

Simple label filtering

python
# Find annotation groups by label (e.g., groups mentioning "blast")
client.fetch_index("ann_group_index")
client.sql_query("""
    SELECT
        g.SeriesInstanceUID,
        g.AnnotationGroupLabel,
        g.GraphicType,
        g.NumberOfAnnotations,
        g.AlgorithmName
    FROM ann_group_index g
    WHERE LOWER(g.AnnotationGroupLabel) LIKE '%blast%'
    ORDER BY g.NumberOfAnnotations DESC
""")

Label filtering with collection context

python
# Find annotation groups matching a label within a specific collection
client.fetch_index("ann_index")
client.fetch_index("ann_group_index")
client.sql_query("""
    SELECT
        i.collection_id,
        g.AnnotationGroupLabel,
        g.GraphicType,
        g.NumberOfAnnotations,
        g.AnnotationPropertyType_CodeMeaning
    FROM ann_group_index g
    JOIN ann_index a ON g.SeriesInstanceUID = a.SeriesInstanceUID
    JOIN index i ON a.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'your_collection_id'
      AND LOWER(g.AnnotationGroupLabel) LIKE '%keyword%'
    ORDER BY g.NumberOfAnnotations DESC
""")

Annotations on Slide Microscopy (SM + ANN Cross-Reference)

When looking for annotations related to slide microscopy data, use both SM and ANN tables together. The ann_index.referenced_SeriesInstanceUID links each annotation series to its source slide.

python
# Find slide microscopy images and their annotations in a collection
client.fetch_index("sm_index")
client.fetch_index("ann_index")
client.fetch_index("ann_group_index")
client.sql_query("""
    SELECT
        i.collection_id,
        s.ObjectiveLensPower,
        g.AnnotationGroupLabel,
        g.NumberOfAnnotations,
        g.GraphicType
    FROM ann_group_index g
    JOIN ann_index a ON g.SeriesInstanceUID = a.SeriesInstanceUID
    JOIN sm_index s ON a.referenced_SeriesInstanceUID = s.SeriesInstanceUID
    JOIN index i ON a.SeriesInstanceUID = i.SeriesInstanceUID
    WHERE i.collection_id = 'your_collection_id'
    ORDER BY g.NumberOfAnnotations DESC
""")

Join Patterns

SM join (slide microscopy details with collection context)

python
client.fetch_index("sm_index")
result = client.sql_query("""
    SELECT i.collection_id, i.PatientID, s.ObjectiveLensPower, s.min_PixelSpacing_2sf
    FROM index i
    JOIN sm_index s ON i.SeriesInstanceUID = s.SeriesInstanceUID
    LIMIT 10
""")

ANN join (annotation groups with collection context)

python
client.fetch_index("ann_index")
client.fetch_index("ann_group_index")
result = client.sql_query("""
    SELECT
        i.collection_id,
        g.AnnotationGroupLabel,
        g.GraphicType,
        g.NumberOfAnnotations,
        a.referenced_SeriesInstanceUID as source_series
    FROM ann_group_index g
    JOIN ann_index a ON g.SeriesInstanceUID = a.SeriesInstanceUID
    JOIN index i ON a.SeriesInstanceUID = i.SeriesInstanceUID
    LIMIT 10
""")

The following tools work with DICOM format for digital pathology workflows:

Python Libraries:

  • highdicom - High-level DICOM abstractions for Python. Create and read DICOM Segmentations (SEG), Structured Reports (SR), and parametric maps for pathology and radiology. Developed by IDC.
  • wsidicom - Python package for reading DICOM WSI datasets. Parses metadata into easy-to-use dataclasses for whole slide image analysis.
  • TIA-Toolbox - End-to-end computational pathology library with DICOM support via DICOMWSIReader. Provides tile extraction, feature extraction, and pretrained deep learning models.
  • EZ-WSI-DICOMweb - Extract image patches from DICOM whole slide images via DICOMweb. Designed for AI/ML workflows with cloud DICOM stores.

Viewers:

  • Slim - Web-based DICOM slide microscopy viewer and annotation tool. Supports brightfield and multiplexed immunofluorescence imaging via DICOMweb. Developed by IDC.
  • QuPath - Cross-platform open source software for whole slide image analysis. Supports DICOM WSI via Bio-Formats and OpenSlide (v0.4.0+).

Conversion:

  • dicom_wsi - Python implementation for converting proprietary WSI formats to DICOM-compliant files.