Datasets:

tyluan
/

Endovis2017

	---
	license: cc-by-4.0
	task_categories:
	- image-segmentation
	tags:
	- medical
	- surgical-instruments
	- endoscopy
	- robotic-surgery
	- image-segmentation
	pretty_name: Endovis 2017 - Robotic Instrument Segmentation
	size_categories:
	- 1K<n<10K
	dataset_info:
	features:
	- name: image
	dtype: image
	- name: label
	dtype: image
	- name: image_id
	dtype: string
	- name: split
	dtype: string
	- name: file_name
	dtype: string
	- name: relative_path
	dtype: string
	- name: sequence_id
	dtype: int64
	splits:
	- name: train
	num_bytes: 1889651647
	num_examples: 1800
	- name: val
	num_bytes: 945876232
	num_examples: 901
	download_size: 2026707643
	dataset_size: 2835527879
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: val
	path: data/val-*
	---

	# Dataset Card for Endovis2017

	## Dataset Description

	### Dataset Summary

	The Endovis2017 dataset contains preprocessed data for surgical instrument segmentation in robotic endoscopic procedures. This dataset was part of the MICCAI 2017 EndoVis Challenge for robotic instrument segmentation.

	The dataset includes high-resolution images from the da Vinci surgical system along with pixel-level segmentation annotations for surgical instruments. It is designed for training and evaluating computer vision models for surgical scene understanding and instrument tracking.

	### Supported Tasks

	- Image Segmentation: Pixel-level segmentation of surgical instruments in endoscopic images
	- Medical Image Analysis: Understanding surgical scenes and instrument types
	- Computer-Assisted Surgery: Real-time instrument detection and tracking

	### Languages

	Not applicable (image dataset)

	## Dataset Structure

	### Data Instances

	Each instance in the dataset contains:

	```python
	{
	'image': PIL.Image, # RGB endoscopic image
	'label': PIL.Image, # Segmentation mask (grayscale)
	'image_id': str, # Unique identifier
	'file_name': str, # Original filename
	'split': str, # 'train' or 'val'
	'relative_path': str, # Path relative to dataset root
	'sequence_id': int # Sequence/video ID (0 for train, 1-4 for val)
	}
	```

	### Data Fields

	- `image`: RGB image of size 640×480 or similar (varies by sequence)
	- `label`: Grayscale segmentation mask matching image dimensions
	- `image_id`: Unique string identifier for the image
	- `file_name`: Original filename (e.g., "frame000.png")
	- `split`: Dataset split ("train" or "val")
	- `relative_path`: Path relative to dataset root directory
	- `sequence_id`: Integer identifying the surgical sequence (0 for training, 1-4 for validation sequences)

	### Data Splits

	\| Split \| Examples \|
	\|-------\|----------\|
	\| train \| 1,800 \|
	\| val \| 901 \|
	\| Total \| 2,701 \|

	The training set contains images from multiple surgical procedures, while the validation set is organized into 4 different sequences (val1-val4) representing different surgical scenarios.

	## Dataset Creation

	### Source Data

	The dataset originates from the 2017 Robotic Instrument Segmentation Challenge held at MICCAI 2017.

	Original Source: [Zenodo Repository](https://zenodo.org/records/10527017)

	#### Data Collection

	Images were captured using the da Vinci surgical system during robotic-assisted surgical procedures. The dataset includes various instrument types and surgical scenarios to ensure model generalization.

	#### Annotations

	Pixel-level segmentation masks were manually annotated by experts. The annotations include:
	- Binary segmentation (instrument vs. background)
	- Part-level segmentation (shaft, wrist, claspers)
	- Instrument type classification

	### Personal and Sensitive Information

	The dataset contains surgical video frames but does not include patient-identifiable information. All images show only the surgical field and instruments, not patients.

	## Considerations for Using the Data

	### Social Impact

	This dataset enables research in computer-assisted surgery and robotic surgery, which can potentially:
	- Improve surgical outcomes through better instrument tracking
	- Enable automated surgical skill assessment
	- Advance autonomous surgical robotics

	### Bias and Limitations

	- Limited to da Vinci surgical system (may not generalize to other platforms)
	- Contains only certain types of surgical procedures
	- Annotation quality may vary across different sequences
	- Dataset size is relatively small compared to natural image datasets

	### Recommendations

	Users should:
	- Test models on multiple surgical systems if deploying in production
	- Consider domain adaptation techniques for different surgical contexts
	- Validate performance on institution-specific data before clinical use
	- Be aware of potential biases toward specific instrument types and surgical scenarios

	## Usage

	### Loading the Dataset

	```python
	from datasets import load_dataset

	# Download and cache the full dataset
	dataset = load_dataset("tyluan/Endovis2017")

	# Access splits
	train_data = dataset['train']
	val_data = dataset['val']

	# Get a sample
	sample = train_data[0]
	image = sample['image'] # PIL Image
	label = sample['label'] # PIL Image (segmentation mask)

	print(f"Image size: {image.size}")
	print(f"Label size: {label.size}")
	```

	### Streaming Mode (No Download)

	For quick exploration without downloading the entire dataset:

	```python
	from datasets import load_dataset

	# Stream the dataset
	dataset = load_dataset("tyluan/Endovis2017", streaming=True)

	# Iterate over samples
	for sample in dataset['train']:
	image = sample['image']
	label = sample['label']
	# Process sample...
	break # Just show first sample
	```

	### Using with PyTorch

	```python
	from datasets import load_dataset
	from torch.utils.data import DataLoader
	from torchvision import transforms

	# Load dataset
	dataset = load_dataset("tyluan/Endovis2017", split="train")

	# Define transforms
	transform = transforms.Compose([
	transforms.Resize((256, 256)),
	transforms.ToTensor(),
	])

	# Apply transforms
	def apply_transforms(example):
	example['image'] = transform(example['image'])
	example['label'] = transform(example['label'])
	return example

	dataset = dataset.map(apply_transforms)
	dataset.set_format(type='torch', columns=['image', 'label'])

	# Create DataLoader
	dataloader = DataLoader(dataset, batch_size=8, shuffle=True)

	# Iterate
	for batch in dataloader:
	images = batch['image'] # Shape: [8, 3, 256, 256]
	labels = batch['label'] # Shape: [8, 1, 256, 256]
	# Train your model...
	break
	```

	### Integration with EasyMedSeg

	This dataset is part of the EasyMedSeg framework:

	```python
	from dataloader.image import Endovis2017Dataset

	# Download mode (recommended)
	dataset = Endovis2017Dataset(
	mode='download',
	split='train',
	hf_repo_id='tyluan/Endovis2017'
	)

	# Streaming mode
	from dataloader.image import Endovis2017StreamingDataset

	streaming_dataset = Endovis2017StreamingDataset(
	split='val',
	shuffle=True
	)
	```

	## Additional Information

	### Dataset Curators

	Original dataset curated by the MICCAI 2017 EndoVis Challenge organizers.

	HuggingFace version prepared by the EasyMedSeg team.

	### Licensing Information

	This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

	When using this dataset, you must:
	- Give appropriate credit to the original authors
	- Provide a link to the license: https://creativecommons.org/licenses/by/4.0/
	- Indicate if changes were made

	### Citation Information

	If you use this dataset in your research, please cite:

	```bibtex
	@article{allan2019endovis,
	title={2017 Robotic Instrument Segmentation Challenge},
	author={Allan, Max and Shvets, Alex and Kurmann, Thomas and Zhang, Zichen and Duggal, Rahul and Su, Yun-Hsuan and Rieke, Nicola and Laina, Iro and Kalavakonda, Niveditha and Bodenstedt, Sebastian and others},
	journal={arXiv preprint arXiv:1902.06426},
	year={2019}
	}
	```

	### Contributions

	Thanks to:
	- MICCAI 2017 EndoVis Challenge organizers for creating the dataset
	- Original annotators for high-quality segmentation masks
	- EasyMedSeg team for preparing the HuggingFace version

	### Contact

	For questions or issues with this HuggingFace version, please open an issue in the [EasyMedSeg repository](https://github.com/EasyMedSeg/EasyMedSeg).

	For questions about the original dataset, refer to the [challenge website](https://opencas.dkfz.de/endovis/) or the [Zenodo repository](https://zenodo.org/records/10527017).

	## References

	- [MICCAI 2017 EndoVis Challenge](https://opencas.dkfz.de/endovis/)
	- [Original Paper (arXiv:1902.06426)](https://arxiv.org/abs/1902.06426)
	- [Zenodo Repository](https://zenodo.org/records/10527017)
	- [EasyMedSeg Framework](https://github.com/EasyMedSeg/EasyMedSeg)