---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- medical
---

# Kidney-CT-Abnormality

<!-- Provide a quick summary of the dataset. -->
This Kidney-CT-Abnormality dataset consists of kidney CT scans with abnormality label.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
This Kidney-CT-Abnormality dataset comprises a comprehensive collection CT scans focusing on kidney CT abnormality, which can serve as a resource for researchers with this field.
Contained within this dataset are 986 .mha (medical high-resolution image) files, which are all 3D medical iamges. 3D images means multiple layers are included in each image, which can be beneficial for precise classification.


A .json file is also included, illustrating the abnormality status of each image.

Note that this dataset was reconstructed from “Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans” (https://zenodo.org/records/8014290), to fit the 2023 automated universal classification challenges (AUC2023) as stated by the authors.


### Dataset Sources 

<!-- Provide the basic links for the dataset. -->
- **Original Homepage:** https://zenodo.org/records/8043408

## Uses

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This dataset is intended for kidney abnormality classification.

### Direct Use

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### Out-of-Scope Use

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## Dataset Initial Processing


## Dataset Structure

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## Dataset Creation

### Curation Rationale

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Kidney diseases can be challenging to detect, while identification or diagnosis is crucial for treatment. 
This dataset is intended to develop AI and machine learning algorithms for enhanced kidney disease recognition.


### Source Data

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https://zenodo.org/records/8043408

#### Data Collection and Processing

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https://zenodo.org/records/8014290
The original dataset contains “215 thoraxabdomen CT scans with segmentations of the kidney and abnormalities in the kidney”. Note that the original datasets contains in total 38.4G image data in mha format, and there is no .json file indicating the abnormality status. Alternatively, a segmentation kidney image dataset is included.


#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->



### Annotations 

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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

<!-- This section describes the people or systems who created the annotations. -->



#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
The CT scan image file names contain the study ID, indicating the differences in scans. These ids are anonymous and don’t contain any other personal information.

## Some helper functions for further utilization


## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

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Datasets:
Alves, N., & Boulogne, L. (2023). Kidney CT Abnormality [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8043408
Gabriel E. Humpire-Mamani, Luc Builtjes, Colin Jacobs, Bram van Ginneken, Mathias Prokop, & Ernst Th. Scholten. (2023). Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8014290