---
license: mit
task_categories:
- image-segmentation
- image-classification
tags:
- medical
- ct-scan
- radiology
- chest-ct
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: "train/*.parquet"
---

# RadGenome ChestCT Reshaped Tiny Dataset

This dataset contains resized chest CT scans from the RadGenome-ChestCT dataset.

## Dataset Details

- **Original Resolution**: 900x900xN
- **Resized Resolution**: 300x300xN
- **Format**: NIfTI (.nii.gz)
- **Number of Volumes**: 253
- **Space Reduction**: ~89% (resized to 1/9th of original spatial dimensions)

## Dataset Structure

Each entry contains:
- `volumename`: Name of the CT volume file (string)
- `anatomy`: Anatomical region information (string)
- `sentence`: Associated radiology report sentence (string)
- `volume_path`: Relative path to the .nii.gz file (string)

## Columns

| Column | Type | Description |
|--------|------|-------------|
| volumename | string | CT volume filename |
| anatomy | string | Anatomical region |
| sentence | string | Radiology report text |
| volume_path | string | Path to .nii.gz file |

## Usage

```python
from datasets import load_dataset
import nibabel as nib

# Load the dataset
ds = load_dataset("nahidhasan/radgenome-ct-reshaped-tiny")

# Access dataset information
print(f"Number of samples: {len(ds['train'])}")

# Access a single entry
sample = ds['train'][0]
print(f"Volumename: {sample['volumename']}")
print(f"Anatomy: {sample['anatomy']}")
print(f"Sentence: {sample['sentence']}")
print(f"Volume path: {sample['volume_path']}")

# Download and load the NIfTI file
# nii = nib.load(sample['volume_path'])
# data = nii.get_fdata()
# print(f"Volume shape: {data.shape}")
```

## Source

This is a resized subset of the [RadGenome-ChestCT dataset](https://huggingface.co/datasets/RadGenome/RadGenome-ChestCT).

## Processing

CT volumes were resized from 900x900xN to 300x300xN using trilinear interpolation while preserving the slice dimension.