Note
This is a Hugging Face dataset. Learn how to load datasets from the Hub in the Hugging Face integration docs.
Dataset Card for btcv#
This is a FiftyOne dataset with 30 video samples.
Installation#
If you haven’t already, install FiftyOne:
pip install -U fiftyone
Usage#
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/BTCV-CT-as-video-MedSAM2-dataset")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details#
Dataset Description#
This dataset is the “Beyond the Cranial Vault” (BTCV) dataset used by Medical-SAM2 paper. Med-SAM2 fine-tunes the Segment Anything Model 2 on to accurately segment CT-scan imagery.
The paper “adopts the philosophy of taking medical images as videos”; so, the images have been converted into videos, and maybe easily resampled into frames using dataset.to_frames(sample_frames=True).
Curated by: Synapse
Shared by [optional]: Jiayuan Zhu and Med-SAM2 Authors
Dataset Sources [optional]#
Med-SAM2 Github Repository: MedicineToken/Medical-SAM2
Paper: Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Data Repository: Med-SAM2 preprocessed dataset on HF
Demo [optional]: [Coming Soon…]
Uses#
Direct Use#
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Out-of-Scope Use#
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Dataset Structure#
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Dataset Creation#
Curation Rationale#
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Source Data#
Data Collection and Processing#
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Who are the source data producers?#
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Annotations [optional]#
Annotation process#
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Who are the annotators?#
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Personal and Sensitive Information#
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Bias, Risks, and Limitations#
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Recommendations#
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation [optional]#
BibTeX:
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APA:
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Glossary [optional]#
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