Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI
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Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) tends to show higher diagnostic accuracy than other modalities. There is a demand for computer-assisted detection (CAD) software for Gd-EOB-DTPA-enhanced MRI. Segmentation with high accuracy is important for CAD software. We propose a liver segmentation method for Gd-EOB-DTPA-enhanced MRI that is based on a four-dimensional (4D) fully convolutional residual network (FC-ResNet). The aims of this study are to determine the best combination of an input image and output image in our proposed method and to compare our proposed method with the previous rule-based segmentation method.
We prepared a five-phase image set and a hepatobiliary phase image set as the input image sets to determine the best input image set. We also prepared a labeled liver image and labeled liver and labeled body trunk images as the output image sets to determine the best output image set. In addition, we optimized the hyperparameters of our proposed model. We used 30 cases to train our model, 10 cases to determine the hyperparameters of our model, and 20 cases to evaluate our model.
Our network with the five-phase image set and the output image set of labeled liver and labeled body trunk images showed the highest accuracy. Our proposed method showed higher accuracy than the previous rule-based segmentation method. The Dice coefficient of the liver region was 0.944 ± 0.018.
Our proposed 4D FC-ResNet showed satisfactory performance for liver segmentation as preprocessing in CAD software.
KeywordsSegmentation Gd-EOB-DTPA CAD Liver Convolutional neural network
Compliance with ethical standards
Conflict of interest
The authors declare no conflicts of interest with regard to the present study.
- 2.Berger-Kulemann V, Schima W, Baroud S et al (2012) Gadoxetic acid-enhanced 30 T MR imaging versus multidetector-row CT in the detection of colorectal metastases in fatty liver using intraoperative ultrasound and histopathology as a standard of reference. Eur J Surg Radiol 38(2):670–676Google Scholar
- 3.Takenaga T, Hanaoka S, Nemoto M et al. (2017) Segmentation of liver region in Gd-EOB enhanced magnetic resonance images. In: Proceedings of IFMIA 2017 the international forum medical imaging in Asia, Okinawa, pp 19–20Google Scholar
- 6.Christ PF, Ettlinger F, Grun F et al (2017) Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv:1702.05970v2
- 10.Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv:1412.6980
- 11.Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305Google Scholar
- 12.Chainer. http://chainer.org/. Accessed 6 September 2018
- 14.Isensee F, Jaeger PF, Full PM, et al (2017) Automatic cardiac disease assessment on cine-mri via time-series segmentation and domain specific features. In: International workshop on statistical atlases and computational models of the heart, pp 120–129Google Scholar