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Automatic multi-organ segmentation of the dual energy computed tomography(DECT) data can be beneficial for biomedical research and clinicalapplications. However, it is a challenging task. Recent advances in deeplearning showed the feasibility to use 3-D fully convolutional networks (FCN)for voxel-wise dense predictions in single energy computed tomography (SECT).In this paper, we proposed a 3D FCN based method for automatic multi-organsegmentation in DECT. The work was based on a cascaded FCN and a general modelfor the major organs trained on a large set of SECT data. We preprocessed theDECT data by using linear weighting and fine-tuned the model for the DECT data.The method was evaluated using 42 torso DECT data acquired with a clinicaldual-source CT system. Four abdominal organs (liver, spleen, left and rightkidneys) were evaluated. Cross-validation was tested. Effect of the weight onthe accuracy was researched. In all the tests, we achieved an average Dicecoefficient of 93% for the liver, 90% for the spleen, 91% for the right kidneyand 89% for the left kidney, respectively. The results show our method isfeasible and promising.

Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network