| --- |
| 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`. |
|
|
