---
task_categories:
  - object-detection
tags:
  - microscopy
  - biology
  - few-shot
  - cell-detection
  - biomedical
  - malaria
  - blood-cells
  - live-cell-imaging
  - fibroblast
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: example
        path: data/example.parquet
      - split: test
        path: data/test.parquet
---

# Micro-OD

**Micro-OD** is a few-shot microscopy object detection benchmark. It aggregates four publicly available biological imaging datasets across distinct microscopy domains and cell types, and packages them into a standardised format designed for evaluating vision models — in particular, large vision-language models (VLMs) — under few-shot, in-context prompting conditions.

## Motivation

Microscopy object detection is a challenging setting for general-purpose vision models: images are domain-specific, class vocabularies are narrow but fine-grained, and labelled data is scarce. Micro-OD is designed to probe how well a model can detect cells and parasites in a new domain when given only a handful of annotated example images at inference time — without any fine-tuning.

## Dataset Splits

The dataset contains two splits that together constitute the few-shot evaluation protocol:

| Split | Role | Images per sub-dataset | Total images |
|---|---|---|---|
| `example` | Few-shot support set | 10 | 40 |
| `test` | Evaluation query set | 53 | 212 |

**Evaluation protocol:** For each sub-dataset, a model may be provided with up to **10 example images** (from the `example` split) as in-context demonstrations. It is then evaluated on each of the **53 test images** in the `test` split. No fine-tuning on the example images is assumed — they serve solely as few-shot context.

## Sub-datasets

| Sub-dataset | Domain | Classes | Original size | Format | Source |
|---|---|---|---|---|---|
| BBBC | Bright-field blood smear; malaria parasite detection | Red Blood Cells, Trophozoite Cells, Ring Cells, Gametocyte Cells, Schizont Cells, White Blood Cells | 1,328 images | PNG | [Broad Bioimage Benchmark Collection](https://bbbc.broadinstitute.org/) |
| BCCD | Peripheral blood smear; blood cell counting | Red Blood Cells, White Blood Cells, Platelets | 364 images | JPG | [BCCD Dataset](https://github.com/Shenggan/BCCD_Dataset) |
| LIVECell | Phase-contrast live cell imaging (RatC6) | Spindle Cells, Polygonal Cells, Round Cells | 420 images | PNG | [LIVECell](https://sartorius-research.github.io/LIVECell/) (images); annotations in-lab |
| NIH-3T3 | Phase-contrast mouse fibroblast imaging | Polygonal Cells, Spindle Cells, Round Cells | 63 images | PNG | In-lab collection and annotation |

## Folder Structure

```
Micro-OD/
├── data/                           # Generated Parquet files (HuggingFace viewer)
│   ├── example.parquet             # 40 rows — few-shot support set
│   └── test.parquet                # 212 rows — evaluation query set
│
├── example/                        # Few-shot support set (raw files)
│   ├── BBBC/
│   │   ├── annotation.jsonl        # Bounding-box annotations
│   │   ├── images/                 # 10 PNG images
│   │   └── images_overlay/         # 10 images with bounding boxes drawn
│   ├── BCCD/
│   │   ├── annotation.jsonl
│   │   ├── images/                 # 10 JPG images
│   │   └── images_overlay/
│   ├── LIVECell/
│   │   ├── annotation.jsonl
│   │   ├── images/                 # 10 PNG images
│   │   └── images_overlay/
│   ├── NIH-3T3/
│   │   ├── annotation.jsonl
│   │   ├── images/                 # 10 PNG images
│   │   └── images_overlay/
│   └── stat.txt                    # Split-level statistics
│
└── test/                           # Evaluation query set (raw files)
    ├── BBBC/
    │   ├── annotation.jsonl
    │   ├── images/                 # 53 PNG images
    │   └── images_overlay/
    ├── BCCD/
    │   ├── annotation.jsonl
    │   ├── images/                 # 53 JPG images
    │   └── images_overlay/
    ├── LIVECell/
    │   ├── annotation.jsonl
    │   ├── images/                 # 53 PNG images
    │   └── images_overlay/
    ├── NIH-3T3/
    │   ├── annotation.jsonl
    │   ├── images/                 # 53 PNG images
    │   └── images_overlay/
    └── stat.txt                    # Split-level statistics
```

Each `images_overlay/` folder contains copies of the images with ground-truth bounding boxes rendered on top, useful for visual verification.

## Annotation Format

Annotations are stored as [JSON Lines](https://jsonlines.org/) (`.jsonl`) files — one JSON object per line, one line per image.

```json
{
  "image_path": "images/<filename>",
  "bbox": {
    "<class_name>": [
      [[x_min, y_min], [x_max, y_max]],
      [[x_min, y_min], [x_max, y_max]]
    ],
    "<class_name>": [
      [[x_min, y_min], [x_max, y_max]]
    ]
  }
}
```

**Coordinate convention:**
- All coordinates are in **pixel space**.
- Each bounding box is represented as two points: `[x_min, y_min]` (top-left corner) and `[x_max, y_max]` (bottom-right corner).
- A class key is present only if at least one instance of that class appears in the image.

**Concrete example** (from `example/BCCD/annotation.jsonl`):

```json
{
  "image_path": "images/BCCD_example_1.jpg",
  "bbox": {
    "Red Blood Cells": [
      [[201, 223], [314, 322]],
      [[1, 252], [89, 357]],
      [[203, 336], [292, 441]]
    ],
    "White Blood Cells": [
      [[211, 4], [338, 132]]
    ],
    "Platelets": [
      [[330, 442], [373, 480]]
    ]
  }
}
```

## Usage

To load the dataset:

```python
from datasets import load_dataset

ds = load_dataset("stumbledparams/Micro-OD")

# Access splits
example_split = ds["example"]   # 40 images — few-shot support set
test_split    = ds["test"]       # 212 images — evaluation query set

# Each row contains:
#   image       — PIL image
#   image_id    — "<subdataset>/images/<filename>"
#   subdataset  — one of: BBBC, BCCD, LIVECell, NIH-3T3
#   objects     — dict with keys:
#                   bbox     : list of [x_min, y_min, width, height]  (COCO format, float32)
#                   category : list of int  (ClassLabel index; decode with int2str)

row = test_split[0]

# Image (PIL.Image)
image = row["image"]

# Bounding boxes and category indices
bboxes     = row["objects"]["bbox"]      # list of [x_min, y_min, width, height] (float32)
categories = row["objects"]["category"]  # list of int (ClassLabel index)

# Class names in ClassLabel index order (alphabetically sorted)
CLASS_NAMES = [
    "Gametocyte Cells", "Platelets", "Polygonal Cells", "Red Blood Cells",
    "Ring Cells", "Round Cells", "Schizont Cells", "Spindle Cells",
    "Trophozoite Cells", "White Blood Cells",
]

for bbox, cat_idx in zip(bboxes, categories):
    x_min, y_min, width, height = bbox
    label = CLASS_NAMES[cat_idx]
    print(f"{label}: [{x_min:.1f}, {y_min:.1f}, {width:.1f}, {height:.1f}]")
```

> **Note on bbox format:** The Parquet files store bboxes in COCO format `[x_min, y_min, width, height]` as float32. `category` is stored as a `ClassLabel` integer index. The raw `annotation.jsonl` files use `[[x_min, y_min], [x_max, y_max]]` (top-left / bottom-right pixel coordinates) — see [Annotation Format](#annotation-format).

## Dataset Statistics

Detailed per-class statistics are available in `example/stat.txt` and `test/stat.txt`. Summaries are provided below.

### Test Split — 212 images

| Sub-dataset | Images | Classes | Total boxes | Boxes/image (mean) | Boxes/image (range) |
|---|---|---|---|---|---|
| BBBC | 53 | 6 | 4,000 | 75.5 | 19–135 |
| BCCD | 53 | 3 | 952 | 18.0 | 9–30 |
| LIVECell | 53 | 3 | 223 | 4.2 | 1–15 |
| NIH-3T3 | 53 | 3 | 376 | 7.1 | 1–14 |
| **Total** | **212** | **10** | **5,551** | — | — |

### Example Split — 40 images

The **Support-Spread Score** (SS) is a composite metric reflecting both class coverage (fraction of classes represented in the sample) and class balance (how evenly instances are distributed across represented classes). Higher is better; a score of 1.0 indicates perfect coverage and balance.

| Sub-dataset | Images | Total boxes | Boxes/image (mean) | Support-Spread Score |
|---|---|---|---|---|
| BBBC | 10 | 734 | 73.4 | 0.136 |
| BCCD | 10 | 78 | 7.8 | 0.680 |
| LIVECell | 10 | 40 | 4.0 | 0.763 |
| NIH-3T3 | 10 | 62 | 6.2 | 0.612 |
| **Total** | **40** | **914** | — | — |

The low SS for BBBC (0.136) reflects the extreme dominance of Red Blood Cells in the malaria dataset, which makes it difficult to achieve a balanced 10-image sample across all 6 classes.

### Class Inventory

| Class | Sub-dataset(s) | Test boxes |
|---|---|---|
| Gametocyte Cells | BBBC | 24 |
| Platelets | BCCD | 159 |
| Polygonal Cells | LIVECell, NIH-3T3 | 417 |
| Red Blood Cells | BBBC, BCCD | 4,427 |
| Ring Cells | BBBC | 34 |
| Round Cells | LIVECell, NIH-3T3 | 24 |
| Schizont Cells | BBBC | 10 |
| Spindle Cells | LIVECell, NIH-3T3 | 158 |
| Trophozoite Cells | BBBC | 193 |
| White Blood Cells | BBBC, BCCD | 105 |

Note that Polygonal Cells, Round Cells, and Spindle Cells appear in both LIVECell and NIH-3T3 but describe morphologically similar — not biologically identical — phenotypes in different cell lines.

## Attribution

Micro-OD combines images and annotations from multiple sources. Please credit the original sources as appropriate:

- **BBBC (malaria):** Ljosa, V., Sokolnicki, K. L., & Carpenter, A. E. (2012). Annotated high-throughput microscopy image sets for validation. *Nature Methods*, 9(7), 637. [https://bbbc.broadinstitute.org/](https://bbbc.broadinstitute.org/)

- **BCCD:** Shenggan. *BCCD Dataset*. GitHub. [https://github.com/Shenggan/BCCD_Dataset](https://github.com/Shenggan/BCCD_Dataset)

- **LIVECell (images):** Edlund, C., et al. (2021). LIVECell — A large-scale dataset for label-free live cell segmentation. *Nature Methods*, 18(9), 1038–1045. [https://doi.org/10.1038/s41592-021-01249-6](https://doi.org/10.1038/s41592-021-01249-6). The morphology-based bounding-box annotations used in Micro-OD were produced in-lab and are not part of the original LIVECell release.

- **NIH-3T3:** Images and bounding-box annotations are an in-lab collection and are not sourced from a public dataset.