  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.
