---
license: mit
dataset_info:
  - config_name: classification
    features:
      - name: img_path
        dtype: string
      - name: bowel
        dtype:
          class_label:
            names:
              "0": healthy
              "1": injury
      - name: extravasation
        dtype:
          class_label:
            names:
              "0": healthy
              "1": injury
      - name: kidney
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: liver
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: spleen
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: any_injury
        dtype: bool
      - name: metadata
        struct:
          - name: series_id
            dtype: int32
          - name: patient_id
            dtype: int32
          - name: incomplete_organ
            dtype: bool
          - name: aortic_hu
            dtype: float32
          - name: pixel_representation
            dtype: int32
          - name: bits_allocated
            dtype: int32
          - name: bits_stored
            dtype: int32
    splits:
      - name: train
        num_bytes: 802231
        num_examples: 4239
      - name: test
        num_bytes: 89326
        num_examples: 472
    download_size: 96729254048
    dataset_size: 891557
  - config_name: classification-with-mask
    features:
      - name: img_path
        dtype: string
      - name: seg_path
        dtype: string
      - name: bowel
        dtype:
          class_label:
            names:
              "0": healthy
              "1": injury
      - name: extravasation
        dtype:
          class_label:
            names:
              "0": healthy
              "1": injury
      - name: kidney
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: liver
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: spleen
        dtype:
          class_label:
            names:
              "0": healthy
              "1": low
              "2": high
      - name: any_injury
        dtype: bool
      - name: metadata
        struct:
          - name: series_id
            dtype: int32
          - name: patient_id
            dtype: int32
          - name: incomplete_organ
            dtype: bool
          - name: aortic_hu
            dtype: float32
          - name: pixel_representation
            dtype: int32
          - name: bits_allocated
            dtype: int32
          - name: bits_stored
            dtype: int32
    splits:
      - name: train
        num_bytes: 58138
        num_examples: 185
      - name: test
        num_bytes: 6600
        num_examples: 21
    download_size: 4196738529
    dataset_size: 64738
  - config_name: segmentation
    features:
      - name: img_path
        dtype: string
      - name: seg_path
        dtype: string
      - name: metadata
        struct:
          - name: series_id
            dtype: int32
          - name: patient_id
            dtype: int32
          - name: incomplete_organ
            dtype: bool
          - name: aortic_hu
            dtype: float32
          - name: pixel_representation
            dtype: int32
          - name: bits_allocated
            dtype: int32
          - name: bits_stored
            dtype: int32
    splits:
      - name: train
        num_bytes: 50714
        num_examples: 185
      - name: test
        num_bytes: 5757
        num_examples: 21
    download_size: 4196631843
    dataset_size: 56471
task_categories:
  - image-classification
  - image-segmentation
pretty_name: RSNA 2023 Abdominal Trauma Detection (Preprocessed)
size_categories:
  - 1K<n<10K
---

# Dataset Card for RSNA 2023 Abdominal Trauma Detection (Preprocessed)

## Dataset Description

- **Homepage:** [https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection](https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection)
- **Source:** [https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data)

### Dataset Summary

This dataset is the preprocessed version of the dataset from [RSNA 2023 Abdominal Trauma Detection Kaggle Competition](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data).

It is tailored for segmentation and classification tasks. It contains 3 different configs as described below:

- **classification**:
  - 4711 instances where each instance includes a CT scan in NIfTI format, target labels, and its relevant metadata.
- **segmentation**:
  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata.
- **classification-with-mask**:
  - 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels, and its relevant metadata.

All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size.

### Usage

```python
from datasets import load_dataset

# Classification dataset
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=True) # "classification" is the default configuration
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=False) # download dataset and cache locally (~90.09 GiB)
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True, test_size=0.05, random_state=42) # specify split size for train-test split

# Classification dataset with segmentation masks
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False) # download dataset and cache locally (~3.91 GiB)
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False, test_size=0.05, random_state=42) # specify split size for train-test split

# Segmentation dataset
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=False) # download dataset and cache locally (~3.91 GiB)
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True, test_size=0.1, random_state=42) # specify split size for train-test split

# Get the dataset splits
train_rsna_cls_ds = rsna_cls_ds["train"]; test_rsna_cls_ds = rsna_cls_ds["test"]
train_rsna_clsmask_ds = rsna_clsmask_ds["train"]; test_rsna_clsmask_ds = rsna_clsmask_ds["test"]
train_rsna_seg_ds = rsna_seg_ds["train"]; test_rsna_seg_ds = rsna_seg_ds["test"]

# Tip: Download speed up with multiprocessing
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=False, num_proc=8) # num_proc: num of cpu core used for loading the dataset
```

## Dataset Structure

### Data Instances

#### Configuration 1: classification

- **Size of downloaded dataset files:** 90.09 GiB

An example of an instance in the 'classification' configuration looks as follows:

```json
{
  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25899/21872.nii.gz",
  "bowel": 0,
  "extravasation": 0,
  "kidney": 0,
  "liver": 0,
  "spleen": 0,
  "any_injury": false,
  "metadata": {
    "series_id": 21872,
    "patient_id": 25899,
    "incomplete_organ": false,
    "aortic_hu": 113.0,
    "pixel_representation": 0,
    "bits_allocated": 16,
    "bits_stored": 12
  }
}
```

#### Configuration 2: segmentation

- **Size of downloaded dataset files:** 3.91 GiB

An example of an instance in the 'segmentation' configuration looks as follows:

```json
{
  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
  "seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
  "metadata": {
    "series_id": 4622,
    "patient_id": 4791,
    "incomplete_organ": false,
    "aortic_hu": 223.0,
    "pixel_representation": 1,
    "bits_allocated": 16,
    "bits_stored": 16
  }
}
```

#### Configuration 3: classification-with-mask

- **Size of downloaded dataset files:** 3.91 GiB

An example of an instance in the 'classification-with-mask' configuration looks as follows:

```json
{
  "img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
  "seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
  "bowel": 0,
  "extravasation": 0,
  "kidney": 0,
  "liver": 1,
  "spleen": 1,
  "any_injury": true,
  "metadata": {
    "series_id": 4622,
    "patient_id": 4791,
    "incomplete_organ": false,
    "aortic_hu": 223.0,
    "pixel_representation": 1,
    "bits_allocated": 16,
    "bits_stored": 16
  }
}
```

### Data Fields

The data fields for all configurations are as follows:

- `img_path`: a `string` feature representing the path to the CT scan in NIfTI format.
- `seg_path`: a `string` feature representing the path to the segmentation mask in NIfTI format (only for 'segmentation' and 'classification-with-mask' configurations).
- `bowel`, `extravasation`, `kidney`, `liver`, `spleen`: Class label features indicating the condition of respective organs.
- `any_injury`: a `bool` feature indicating the presence of any injury.
- `metadata`: a dictionary feature containing metadata information with the following fields:
  - `series_id`: an `int32` feature.
  - `patient_id`: an `int32` feature.
  - `incomplete_organ`: a `bool` feature.
  - `aortic_hu`: a `float32` feature.
  - `pixel_representation`: an `int32` feature.
  - `bits_allocated`: an `int32` feature.
  - `bits_stored`: an `int32` feature.

### Data Splits

Default split:

- 0.9:0.1 with random_state = 42

| Configuration Name       | Train (n_samples) | Test (n_samples) |
| ------------------------ | ----------------: | ---------------: |
| classification           |              4239 |              472 |
| segmentation             |               185 |               21 |
| classification-with-mask |               185 |               21 |

Modify the split proportion:

```python
rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", test_size=0.05, random_state=42)
```


## Additional Information

### Citation Information

- Preprocessed dataset:

```
@InProceedings{huggingface:dataset,
title = {RSNA 2023 Abdominal Trauma Detection Dataset (Preprocessed)},
author={Hong Jia Herng},
year={2023}
}
```

- Original dataset:

```
@misc{rsna-2023-abdominal-trauma-detection,
    author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan},
    title = {RSNA 2023 Abdominal Trauma Detection},
    publisher = {Kaggle},
    year = {2023},
    url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection}
}
```