Automatic multi-organ segmentation in dual energy CT using 3D fully convolutional network

Shuqing Chen, Xia Zhong, Shiyang Hu, Sabrina Dorn, Marc Kachelriess, Michael Lell, Andreas Maier

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.

Author Affiliation: Friedrich-Alexander-Universität Erlangen-Nürnberg, German Cancer Research Center (DKFZ), Ruprecht-Karls-University Heidelberg, University Hospital Nürnberg, Paracelsus Medical University

Keywords: multi-organ segmentation, deep learning, DECT, U-Net, FCN