Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation

TL;DR

The Duke Spleen Data Set provides a diverse collection of CT and MRI images from patients with liver disease, supporting the development of robust deep learning models for spleen segmentation that account for complex clinical variations.

Contribution

This dataset is the first publicly available spleen dataset that includes confounding features like ascites and varices, aiding in training more accurate segmentation algorithms.

Findings

Includes 109 CT and MRI volumes from diverse sources.

Contains a wide range of spleen shapes, sizes, and disease-related features.

Supports development of robust spleen segmentation models.

Abstract

Spleen volumetry is primarily associated with patients suffering from chronic liver disease and portal hypertension, as they often have spleens with abnormal shapes and sizes. However, manually segmenting the spleen to obtain its volume is a time-consuming process. Deep learning algorithms have proven to be effective in automating spleen segmentation, but a suitable dataset is necessary for training such algorithms. To our knowledge, the few publicly available datasets for spleen segmentation lack confounding features such as ascites and abdominal varices. To address this issue, the Duke Spleen Data Set (DSDS) has been developed, which includes 109 CT and MRI volumes from patients with chronic liver disease and portal hypertension. The dataset includes a diverse range of image types, vendors, planes, and contrasts, as well as varying spleen shapes and sizes due to underlying disease states. The DSDS aims to facilitate the creation of robust spleen segmentation models that can take into account these variations and confounding factors.