license: cc-by-nc-sa-4.0
language:
- en
tags:
- medical
Kidney-CT-Abnormality
This Kidney-CT-Abnormality dataset consists of kidney CT scans with abnormality label.
Dataset Details
Dataset Description
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, as stated by the authors, 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).
In a nutshell, the Kidney-CT-Abnormality dataset can potentially can serve for the academic and research community, possibly enhancing studies in medical image processing and diagnostic algorithm development, thereby improving understanding of kidney diseases and diagnostic accuracy through technological advancements.
Dataset Sources
- Original Homepage: https://zenodo.org/records/8043408
Uses
This dataset is intended for kidney abnormality classification.
Direct Use
Out-of-Scope Use
This dataset cannot be utilized for segmentation task since no ground truth data
Dataset Initial Processing
Dataset Structure
Dataset Creation
Curation Rationale
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
https://zenodo.org/records/8043408
Data Collection and Processing
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?
Annotations
Personal and Sensitive Information
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
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
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