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

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