---
pretty_name: Cataract-1K
license: cc-by-4.0
task_categories:
- image-segmentation
- object-detection
tags:
- medical
- ophthalmology
- surgery
- cataract
size_categories:
- 1k<n<10k
---

# Cataract-1K Dataset Processing

This module describes the **Cataract-1K** image dataset, a component of the [LMOD (Large Multimodal Ophthalmology Dataset)](https://kfzyqin.github.io/lmod/) benchmark.

## Dataset Overview

**Cataract-1K** is a comprehensive dataset of cataract surgery videos.
- **Original Repository**: [https://github.com/Negin-Ghamsarian/Cataract-1K](https://github.com/Negin-Ghamsarian/Cataract-1K)
- **Paper and Dataset**: [LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models](https://kfzyqin.github.io/lmod/)

The original dataset is provided in video format. This processing pipeline extracts relevant frames and annotations (bounding boxes, segmentations) into a unified format for training vision models.

## Data Representation

The processed dataset uses the Hugging Face `datasets` library. To maintain data integrity and optimize storage, images are serialized into bytes.

### Image Conversion Note
We utilize `features.image.image_to_bytes` from the `datasets` library to convert image objects into byte streams during processing.

### Accessing Images
You can decode the image bytes back into visual formats using the standard `PIL` and `io` libraries.

**Example: Reading an image from the dataset**

```python
import io
from PIL import Image as pil_image
import numpy as np

# Access the byte stream of the first sample
image_bytes = ds[0]["image"]["bytes"]

# Decode to PIL Image
image = pil_image.open(io.BytesIO(image_bytes))

# Convert to NumPy array for processing
image_array = np.array(image)
```

## Experimental Setup & Splitting

### 5-Fold Cross-Validation
To ensure rigorous evaluation and comparability with prior work, we recommend employing a **5-fold cross-validation** strategy.

**Critical Note on Data Leakage**:
The dataset contains multiple frames from the same surgery. Splits must be performed at the **case (surgery) level**, not the frame level.

- **Grouping**: Aggregate all frames belonging to the same `case_id`.
- **Splitting**: Partition the unique `case_id`s into 5 exclusive folds.
- **Assignment**: All frames associated with a `case_id` should reside in the same fold.

This approach prevents temporally correlated frames from the same surgery appearing in both training and validation sets, ensuring a fair assessment of model generalization.

## License

The datasets are licensed under Creative Commons 4.0 International (CC BY, Creative Commons License).

## References

If you use this dataset, please cite the following papers:

```bibtex
@inproceedings{Cataract-1K,
    author    = {Negin Ghamsarian and
                Yosuf El-Shabrawi and
                Sahar Nasirihaghighi and
                Doris Putzgruber-Adamitsch and
                Martin Zinkernagel and
                Sebastian Wolf and
                Klaus Schoeffmann and
                Raphael Sznitman},
    title     = {Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection (to appear)},
}
```

```bibtex
@misc{qin2025lmodlargemultimodalophthalmology,
      title={LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models}, 
      author={Zhenyue Qin and Yu Yin and Dylan Campbell and Xuansheng Wu and Ke Zou and Yih-Chung Tham and Ninghao Liu and Xiuzhen Zhang and Qingyu Chen},
      year={2025},
      eprint={2410.01620},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.01620}, 
}
```

NOTE: This documentation is generated using Gemini 3 Pro and verified by human.