  The findings of splenomegaly, abnormal enlargement of the spleen, is a
non-invasive clinical biomarker for liver and spleen disease. Automated
segmentation methods are essential to efficiently quantify splenomegaly from
clinically acquired abdominal magnetic resonance imaging (MRI) scans. However,
the task is challenging due to (1) large anatomical and spatial variations of
splenomegaly, (2) large inter- and intra-scan intensity variations on
multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this
paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce
the deep convolutional neural network (DCNN) approaches in multi-modal MRI
splenomegaly segmentation. Large convolutional kernel layers were used to
address the spatial and anatomical variations, while the conditional generative
adversarial networks (GAN) were employed to leverage the segmentation
performance of SS-Net in an end-to-end manner. A clinically acquired cohort
containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans
was used to train and evaluate the performance of multi-atlas segmentation
(MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results,
the DCNN methods achieved superior performance to the state-of-the-art MAS
method. The proposed SS-Net method achieved the highest median and mean Dice
scores among investigated baseline DCNN methods.
