Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation
This provides a domain-specific dataset for medical imaging researchers to improve spleen segmentation in patients with chronic liver disease, but it is incremental as it addresses a data gap rather than introducing new methods.
The authors tackled the lack of publicly available datasets for spleen segmentation that include confounding features like ascites and abdominal varices by developing the Duke Spleen Data Set (DSDS), which includes 109 CT and MRI volumes from patients with chronic liver disease and portal hypertension to facilitate robust model training.
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.