---
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}
}
```