---
dataset_info:
  features:
  - name: image_id
    dtype: string
  - name: split
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: instance_mask
    dtype: image
  - name: overlay
    dtype: image
  - name: num_instances
    dtype: int32
  - name: height
    dtype: int32
  - name: width
    dtype: int32
  - name: is_mimm_subset
    dtype: bool
  - name: acquisition
    dtype: string
  splits:
  - name: train
    num_bytes: 1388742104
    num_examples: 298
  - name: validation
    num_bytes: 916782499
    num_examples: 199
  download_size: 2305644717
  dataset_size: 2305524603
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
pretty_name: SegPC-2021
license: cc-by-nc-sa-4.0
size_categories:
- n<1K
tags:
- medical
- microscopy
- multiple-myeloma
- plasma-cells
- cell-segmentation
- hematology
task_categories:
- image-segmentation
---
# SegPC-2021 — Segmentation of Multiple Myeloma Plasma Cells

Public mirror of the **ISBI-2021 SegPC challenge** dataset (instance segmentation of Multiple
Myeloma plasma cells in microscopic images), created by **SBILab, IIIT-Delhi & AIIMS New Delhi**
(Anubha Gupta, Ritu Gupta et al.). Mirrored for the EasyMedSeg / MedSphere segmentation benchmark.

## What this is
Brightfield microscopy of **bone-marrow aspirate** slides from Multiple Myeloma patients,
**Jenner–Giemsa** stained and **stain-color-normalized** (GCTI-SN). The task is to segment each
**cell of interest** (cytoplasm + nucleus).

## Splits (only ground-truth-bearing data is mirrored)
| split        | images | notes |
|--------------|--------|-------|
| `train`      | 298    | full train set, all with GT |
| `validation` | 199    | source has 200 images; `610.bmp` ships **no mask** and is dropped |

The challenge **test split (277 images) is excluded** — its ground truth is withheld by the
organizers (test was leaderboard-only).

## Acquisition / image sizes
Two cameras were used: **Olympus** (2040×1536) and **Nikon** (2560×1920). The Nikon images are
flagged `is_mimm_subset = true` — see the overlap warning below.

## Columns
- `image` — RGB microscopy image (PNG, pixel-lossless re-encode of the source BMP).
- `mask` — **semantic** ground truth, `{0 = background, 1 = cytoplasm, 2 = nucleus}`.
  - whole-cell GT = `mask > 0`; nucleus GT = `mask == 2`; cytoplasm-only = `mask == 1`.
- `instance_mask` — `{0 = background, 1..N}`, a distinct id per cell of interest.
- `overlay` — half-resolution viewer aid (nucleus=red, cytoplasm=green); **not** ground truth.
- `image_id`, `split`, `num_instances`, `height`, `width`, `is_mimm_subset`, `acquisition`.

### Mask encoding note (reformatting)
The original release ships **one grayscale BMP per cell instance** (`y/<id>_<k>.bmp`) with raw
values `{0, 20, 40}` (background / cytoplasm / nucleus). This mirror **reformats** those into the
per-image `mask` (semantic 0/1/2) and `instance_mask` (instance ids) columns above. No pixels are
otherwise altered.

## ⚠️ "Cells of interest" caveat
Ground truth is provided **only for designated cells of interest** — a microscopic field may
contain additional plasma cells that are intentionally left unlabeled (treated as background).
The official challenge metric evaluates cells of interest only.

## ⚠️ Cross-dataset overlap (evaluation leakage)
The **Nikon 2560×1920 images** (`is_mimm_subset = true`) correspond to the **MiMM_SBILab** dataset,
and SegPC shares the same SBILab/AIIMS Jenner-Giemsa MM pipeline as the **SN-AM** dataset. No
per-image cross-reference table is published; **dedup by image content** before co-benchmarking
SegPC-2021 against MiMM_SBILab or SN-AM(MM).

## License
**CC BY-NC-SA 4.0** — non-commercial, share-alike, attribution required. By using this data you
agree to cite the publications below.

## Citation
1. A. Gupta, R. Duggal, S. Gehlot, R. Gupta, A. Mangal, L. Kumar, N. Thakkar, D. Satpathy,
   *"GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic
   Medical Images,"* Medical Image Analysis 65, 2020. doi:10.1016/j.media.2020.101788
2. S. Gehlot, A. Gupta, R. Gupta, *"EDNFC-Net: Convolutional Neural Network with Nested Feature
   Concatenation for Nuclei-Instance Segmentation,"* ICASSP 2020, pp. 1389–1393.
3. A. Gupta, P. Mallick, O. Sharma, R. Gupta, R. Duggal, *"PCSeg: Color model driven probabilistic
   multiphase level set based tool for plasma cell segmentation in multiple myeloma,"* PLoS ONE
   13(12): e0207908, 2018. doi:10.1371/journal.pone.0207908
4. A. Gupta, S. Gehlot, S. Goswami, et al., *"SegPC-2021: A challenge & dataset on segmentation of
   Multiple Myeloma plasma cells from microscopic images,"* Medical Image Analysis 83, 2023.
   doi:10.1016/j.media.2022.102677

Dataset DOI: 10.21227/7np1-2q42 (IEEE DataPort). Source: Kaggle `sbilab/segpc2021dataset`.