Tomomi Takenaga1,2, Shouhei Hanaoka3, Yukihiro Nomura4, Mitsutaka Nemoto5, Masaki Murata4, Takahiro Nakao6, Soichiro Miki4, Takeharu Yoshikawa4, Naoto Hayashi4, Osamu Abe3,6. 1. Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. takenaga-tky@umin.ac.jp. 2. Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, 4669-2, Ami, Ami-machi, Inashiki-gun, Ibaraki, Japan. takenaga-tky@umin.ac.jp. 3. Department of Radiology, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 4. Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 5. Faculty of Biology-Oriented Science and Technology, Kindai University, Nishimitani 930, Kinokawa, Wakayama, 649-6493, Japan. 6. Radiology and Biomedical Engineering, Graduate School of Medicine, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Our proposed 4D FC-ResNet showed satisfactory performance for liver segmentation as preprocessing in CAD software.
PURPOSE: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. METHODS: 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. RESULTS: 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. CONCLUSION: Our proposed 4D FC-ResNet showed satisfactory performance for liver segmentation as preprocessing in CAD software.
Authors: Moritz Gross; Michael Spektor; Ariel Jaffe; Ahmet S Kucukkaya; Simon Iseke; Stefan P Haider; Mario Strazzabosco; Julius Chapiro; John A Onofrey Journal: PLoS One Date: 2021-12-01 Impact factor: 3.240