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Spleen volume estimation using automated image segmentation technique may beused to detect splenomegaly (abnormally enlarged spleen) on Magnetic ResonanceImaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN)segmentation methods have demonstrated advantages for abdominal organsegmentation. However, variations in both size and shape of the spleen on MRIimages may result in large false positive and false negative labeling whendeploying DCNN based methods. In this paper, we propose the SplenomegalySegmentation Network (SSNet) to address spatial variations when segmentingextraordinarily large spleens. SSNet was designed based on the framework ofimage-to-image conditional generative adversarial networks (cGAN).Specifically, the Global Convolutional Network (GCN) was used as the generatorto reduce false negatives, while the Markovian discriminator (PatchGAN) wasused to alleviate false positives. A cohort of clinically acquired 3D MRI scans(both T1 weighted and T2 weighted) from patients with splenomegaly were used totrain and test the networks. The experimental results demonstrated that a meanDice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNeton independently tested MRI volumes of patients with splenomegaly.

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks