CT-RATE

April 3, 2024 · View on GitHub

Dataset Information

CT-RATE is a large dataset containing paired chest CT images and corresponding radiological diagnostic reports, along with annotated results for 18 possible abnormal conditions mentioned in the reports. The dataset comprises a total of 50,188 cases, with 47,149 in the training set and 3,039 in the validation set. CT-RATE covers plain scan CT images of 21,304 different patients collected from May 2015 to January 2023. The corresponding radiological reports consist of four parts: clinical information, imaging technique, imaging findings, and impression. Clinical information includes the patient's symptoms and treatment history; imaging technique encompasses imaging protocols, imaging perspectives, and the use of contrast agents; imaging findings include observations anatomically and pathologically, and the impression is given by doctors based on these observations.

CT-RATE is the first open-source dataset to date that includes chest CT images, radiological diagnostic reports, and abnormality labels on a large scale. This dataset can not only help researchers to further train more precise models for generating diagnostic reports for chest CT images, thereby alleviating the workload of doctors; moreover, it is expected to promote the development of large multimodal models in the field of radiology and even general-purpose medical multimodal mega-models.

Dataset Meta Information

Task Type	Language	Train	Val	Test	File Format	Size
Caption	English	47149	3039	-	.nii.gz, .csv	-

Dataset Information Statistics

The CT-RATE data set collects CT image data from three types of CT equipment, including CT equipment produced by Philips, Siemens and Neusoft Philips. The statistical information is as follows:

Manufacturer	Count in train set	Count in internal validation set	Train set ratio	Internal validation set ratio
Philips	28966	1928	0.614	0.634
Siemens	15946	971	0.338	0.320
PNMS	2237	140	0.048	0.046

In the CT-RATE data set, the gender distribution corresponding to different CT images is as follows:

Sex	Count in train set	Count in internal validation set	Train set ratio	Internal validation set ratio
Male	27462	1776	0.583	0.584
Female	19681	1263	0.417	0.416

In addition, the CT-RATE data set counts 18 abnormalities in diagnostic reports, covering common abnormalities in the chest area. The distribution of these 18 abnormalities in the data set is as shown below, including pulmonary nodules, coronary artery walls Calcification, lung opacity and other common abnormalities.

Abnormality	Count in train set	Count in internal validation set	Train set ratio	Internal validation set ratio
Medical material	2387	130	0.102	0.083
Arterial wall calcification	7136	459	0.303	0.296
Cardiomegaly	2604	161	0.114	0.102
Pericardial effusion	718	41	0.030	0.027
Coronary artery wall calcification	6050	388	0.258	0.250
Hiatal hernia	3418	220	0.143	0.139
Lymphadenopathy	5769	385	0.245	0.250
Emphysema	4651	304	0.192	0.197
Atelectasis	6027	363	0.254	0.234
Lung nodule	10892	706	0.450	0.450
Lung opacity	8976	622	0.375	0.398
Pulmonary fibrotic sequela	5809	380	0.240	0.240
Pleural effusion	2967	197	0.126	0.128
Mosaic attenuation pattern	1253	88	0.054	0.057
Peribronchial thickening	1622	109	0.068	0.072
Consolidation	3274	230	0.136	0.145
Bronchiectasis	2028	147	0.084	0.093
Interlobular septal thickening	1674	110	0.070	0.071

Dataset Example

File Structure

The data set file structure is as follows. The file structure of the dataset includes metadata, various anomaly labels, radiology text reports, and CT image files divided into training and validation sets.

Dataset
│
├──── metadata/
│       ├── Metadata_Attributes.xlsx
│       ├── train_metadata.csv
│       └── validation_metadata.csv
├──── multi_abnormality_labels/
│       ├── train_predicted_labels.csv
│       └── valid_predicted_labels.csv
├──── radiology_text_reports/
│       ├── train_reports.csv
│       └── validation_reports.csv
├──── train/
│       ├── train_1/
│       │   ├── train_1_a/
│       │        ├── train_1_a_1.nii.gz
│       │        └── train_1_a_2.nii.gz
│       ├── ...
├──── valid/
│       ├── valid_1/
│       │   ├── valid_1_a/
│       │        ├── valid_1_a_1.nii.gz
│       │        └── valid_1_a_2.nii.gz
│       ├── ...

Authors and Institutions

Ibrahim Ethem Hamamci (University of Zurich, Switzerland)

Sezgin Er (Istanbul Medipol University, Turkey)

Furkan Almas (Istanbul Medipol University, Turkey)

Ayse Gulnihal Simsek (Istanbul Medipol University, Turkey)

Sevval Nil Esirgun (Istanbul Medipol University, Turkey)

Irem Dogan (Istanbul Medipol University, Turkey)

Muhammed Furkan Dasdelen (Istanbul Medipol University, Turkey)

Bastian Wittmann (University of Zurich, Switzerland)

Enis Simsar (ETH Zurich, Switzerland)

Mehmet Simsar (Istanbul Medipol University, Turkey)

Emine Bensu Erdemir (Istanbul Medipol University, Turkey)

Abdullah Alanbay (Istanbul Medipol University, Turkey)

Anjany Sekuboyina (University of Zurich, Switzerland)

Berkan Lafci (University of Zurich, Switzerland)

Mehmet K. Ozdemir (Istanbul Medipol University, Turkey)

Bjoern Menze (University of Zurich, Switzerland)

Source Information

Official Website: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE

Download Link: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE

Article Address: https://arxiv.org/abs/2403.17834

Publication Date: 2024.3

Citation

@ARTICLE{2024arXiv240317834E,
       author = {{Ethem Hamamci}, Ibrahim and {Er}, Sezgin and {Almas}, Furkan and {Gulnihan Simsek}, Ayse and {Nil Esirgun}, Sevval and {Dogan}, Irem and {Furkan Dasdelen}, Muhammed and {Wittmann}, Bastian and {Simsar}, Enis and {Simsar}, Mehmet and {Bensu Erdemir}, Emine and {Alanbay}, Abdullah and {Sekuboyina}, Anjany and {Lafci}, Berkan and {Ozdemir}, Mehmet K. and {Menze}, Bjoern},
        title = {A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities},
      journal = {arXiv preprint arxiv:2403.17834},
         year = 2024,
}

Original introduction article is here.