metadata

annotations_creators: []
language: en
size_categories:
  - n<1K
task_categories: []
task_ids: []
pretty_name: btcv
tags:
  - Med-SAM2
  - Medical-SAM2
  - btcv
  - ct
  - fiftyone
  - fiftyone
  - medical
  - sam2
  - scan
  - segmentation
  - video
description: >-
  The "Beyond the Cranial Vault" (BTCV) dataset used by Medical-SAM2 paper.
  Treats CT scans as a video samples for fine-tuning the Semgent-Anything-2
  model.
dataset_summary: >


  ![image/png](dataset_preview.png)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30
  video samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  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 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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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]

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Dataset Card Authors

Evatt Harvey-Salinger