# Locating fractional DICOM Segmentations in IDC

**URL:** <https://discourse.canceridc.dev/t/locating-fractional-dicom-segmentations-in-idc/776>\
**Category:** Support\
**Tags:** dicom\
**Created:** [January 26, 2026, 3:20pm UTC](https://discourse.canceridc.dev/t/locating-fractional-dicom-segmentations-in-idc/776 "2026-01-26T15:20:00Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**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:** [January 26, 2026, 3:20pm UTC](https://discourse.canceridc.dev/t/locating-fractional-dicom-segmentations-in-idc/776/1 "2026-01-26T15:20:00Z")

</div>

This post is in response to this question that I received from an IDC user recently:

> _“where I can find an example of FRACTIONAL Segmentation in DICOM-SEG?”_

TL;DR: IDC contains **17,238 fractional segmentation series** across multiple radiology and slide microscopy collections.

## What are Fractional Segmentations?

DICOM Segmentation objects support three encoding types, defined by the **Segmentation Type** attribute (0062,0001):

| Type | Values | Overlap Allowed | Description |
| --- | --- | --- | --- |
| **BINARY** | 0 or 1 | Yes | Each voxel is fully inside (1) or outside (0) the segment |
| **FRACTIONAL** | 0 to MaxFractionalValue | Yes | Continuous values representing probability or occupancy |
| **LABELMAP** | Integer segment labels | No | Each voxel belongs to exactly one segment (mutually exclusive) |

### Fractional Sub-types

FRACTIONAL segmentations are further classified by **Segmentation Fractional Type** (0062,0010):

- **PROBABILITY** : Value represents the probability that the segment occupies the voxel
- **OCCUPANCY** : Value represents the proportion of voxel volume occupied by the segment

Values are stored as unsigned 8-bit integers with the maximum value defined by **Maximum Fractional Value** attribute (0062,000E) (up to 255).

### Why Use Fractional Segmentations?

Fractional segmentations are useful for representing:

- **Probability maps** from AI/ML model predictions
- **Partial volume effects** where a voxel contains multiple tissue types
- **Uncertainty quantification** in automated segmentations
- **Continuous measurements** like enhancement ratios in functional imaging

**Note:** as of data release v23, IDC contains only BINARY and FRACTIONAL segmentations; no LABELMAP examples are available.

See [DICOM PS3.3 Section C.8.20.2.3](https://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.8.20.2.3.html) for the normative definition.

## Major Collections with Fractional Segmentations

| collection\_id | Source Modality | Algorithm | Type | Count |
| --- | --- | --- | --- | --- |
| ispy2 | MR | Background Threshold, PE threshold | SEMIAUTOMATIC | 2,688 |
| ispy1 | MR | Background Threshold variants | SEMIAUTOMATIC | 2,568 |
| acrin\_6698 | MR | Background Threshold / Manual | SEMI/MANUAL | 2,213 |
| tcga\_brca | SM | Stony Brook TIL Segmentation | AUTOMATIC | 1,061 |
| breast\_mri\_nact\_pilot | MR | Background Threshold, VOI limits | SEMIAUTOMATIC | 756 |
| tcga\_kirc | SM | Stony Brook TIL Segmentation | AUTOMATIC | 514 |
| tcga\_ucec | SM | Stony Brook TIL Segmentation | AUTOMATIC | 504 |
| tcga\_luad | SM | Stony Brook TIL Segmentation | AUTOMATIC | 479 |

## Two Main Types of Fractional SEGs

1. **Breast MRI (MR) functional tumor mapping** (ISPY1, ISPY2, ACRIN-6698) - Probability maps from DCE-MRI enhancement thresholds.
2. **Slide Microscopy (SM) TIL segmentations** (TCGA collections) - Deep learning-based predictions on pathology slides

## Ready-to-View Examples

### Example 1: Breast MRI (Source Modality: MR)

- **collection\_id:** breast\_mri\_nact\_pilot (part of the I-SPY 1 trial imaging data)
- **Patient:** UCSF-BR-26
- **Algorithm:** Background Threshold, PEthresh and MinConn filter, VOI limits
- **Source Modality:** MR (Breast)
- **License:** CC BY 3.0
- [View DICOM study](https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.3.6.1.4.1.14519.5.2.1.7695.2311.159334962549228603525105118934)

> **Note:** OHIF v3 currently cannot display fractional DICOM SEG, see [handle fractional SEG objects · Issue #1346 · OHIF/Viewers · GitHub](https://github.com/OHIF/Viewers/issues/1346).

### Example 2: Pathology Slide (Source Modality: SM)

- **collection\_id:** tcga\_kirc
- **Patient:** TCGA-CJ-4875
- **Algorithm:** Stony Brook TIL Segmentation Inception-V4 2022
- **Source Modality:** SM (Slide Microscopy)
- **License:** CC BY 4.0
- [View DICOM study](https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.1417858364811608686584744484950100272)

> **Tip:** Generate viewer URLs programmatically with `client.get_viewer_URL(seriesInstanceUID="...")` which auto-selects OHIF for radiology or SLIM for pathology.

## Download an Example

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

# Download a fractional SEG from breast MRI (Source Modality: MR)
# Collection: breast_mri_nact_pilot, Patient: UCSF-BR-26
client.download_from_selection(
    seriesInstanceUID="1.3.6.1.4.1.14519.5.2.1.7695.2311.249901144986078351313294733363",
    downloadDir="./fractional_seg_example_MR"
)

# Download a fractional SEG from pathology slide (Source Modality: SM)
# Collection: tcga_kirc, Patient: TCGA-CJ-4875
client.download_from_selection(
    seriesInstanceUID="1.2.826.0.1.3680043.10.511.3.43729124912062621812911227783921569",
    downloadDir="./fractional_seg_example_SM"
)

```

## Query for All Fractional Segmentations

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

# Find all fractional segmentations with collection info and source modality
results = client.sql_query("""
    SELECT
        i.collection_id,
        src.Modality as source_modality,
        s.SeriesInstanceUID,
        s.AlgorithmName,
        s.AlgorithmType,
        s.total_segments,
        s.segmented_SeriesInstanceUID as source_series
    FROM seg_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    JOIN index src ON s.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE s.SegmentationType = 'FRACTIONAL'
""")
print(f"Found {len(results)} fractional segmentation series")
print(results[['collection_id', 'source_modality', 'AlgorithmName']].head(10))

```

## Verify the Data

Run this to confirm the statistics are current:

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

# Total fractional segmentations
total = client.sql_query("SELECT COUNT(*) FROM seg_index WHERE SegmentationType = 'FRACTIONAL'")
print(f"Total fractional SEGs: {total.iloc[0,0]}")

# IDC version
print(f"IDC version: {client.get_idc_version()}")

```

* * *

If you made it all the way to this point - you get a bonus: this blog post was generated using the [`imaging-data-commons` Claude skill](https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/imaging-data-commons) that we recently added here: [GitHub - K-Dense-AI/claude-scientific-skills: A set of ready to use scientific skills for Claude](https://github.com/K-Dense-AI/claude-scientific-skills) (with several follow up prompts, not from the first try yet), followed by some relatively minor manual edits. I will follow up with a separate post on that skill, but you are welcome to give it a try right away and share your feedback with me!