Abstract: Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data is beneficial for biomedical research and clinical applications. Numerous recent researches in medical image processing show the feasibility to use 3-D fully convolutional networks (FCN) for voxel-wise dense predictions of medical images. In the scope of this work, three 3D-FCN-based algorithmic approaches for the automatic multi-organ segmentation in DECT are developed. Both of the theoretical benefit and the practical performance of these novel deep-learning-based approaches are assessed. The approaches were evaluated using 26 torso DECT data acquired with a clinical dual-source CT system. Six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a cross-validation strategy. In all the tests, we achieved the best average Dice coefficients of 98% for the right lung, 97% for the left lung, 93% for the liver, 91% for the spleen, 94% for the right kidney, 92% for the left kidney, respectively. Successful tests on special clinical cases reveal the high adaptability of our methods in the practical application. The results show that our methods are feasible and promising.
Keywords: multi-organ segmentation, deep learning, DECT, U-Net, FCN
Author Affiliation: Friedrich-Alexander-Universität Erlangen-Nürnberg, German Cancer Research Center (DKFZ), Ruprecht-Karls-University Heidelberg, University Hospital Nürnberg, Paracelsus Medical University