[1]LING Tong,YANG Wanqi,YANG Ming.Prostate segmentation in CT images with multimodal U-net[J].CAAI Transactions on Intelligent Systems,2018,13(6):981-988.[doi:10.11992/tis.201806012]
Copy

Prostate segmentation in CT images with multimodal U-net

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 981-988 Column: 学术论文—机器感知与模式识别 Public date: 2018-10-25
Title:
Prostate segmentation in CT images with multimodal U-net
Author(s):
LING Tong;  YANG Wanqi;  YANG Ming
School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
Keywords:
CT; MRI; deep learning; multimodal U-net; single modal U-net; transfer learning; loss function; prostate segmentation
CLC:
TP18;R318
DOI:
10.11992/tis.201806012
Abstract:
Computed tomography (CT) can be applied to prostate cancer diagnosis; however, it is not effective for the visualization of soft tissues structures because of the resulting low contrast, and thus, it is difficult to perform accurate prostate segmentation in CT images. Contrarily, nuclear magnetic resonance imaging (MRI) provides a relatively high contrast to soft tissues, which can provide rich image information for prostate segmentation. To improve the accuracy of prostate segmentation in CT images, a novel multimodal U-net (MM-unet) is proposed based on deep learning, which fully utilizes the complementary information between MRI and CT images. A transfer learning method is first applied to train the initial segmentation model parameters for segmenting MRI and CT images, and then a novel multimodal loss function MM-Loss is proposed to connect the segmentation models between different modalities, jointly training the proposed MM-unet in paired MRI and CT images. To validate the effectiveness of the proposed MM-unet, we carried out experiments on the prostate dataset provided by our allied hospital. The experimental results show that MM-unet can achieve 3% higher Dice than U-net for prostate segmentation in CT images.
References:
[1] 黄高贤. 前列腺癌中CT、MR诊断的研究进展[J]. 吉林医学, 2010, 31(4):529-531 HUANG Gaoxian. Progress in diagnosis of CT and MR in prostate cancer[J]. Jilin medical journal, 2010, 31(4):529-531
[2] 陈识, 宋曼. MRI与CT诊断不同病理分期前列腺癌患者的准确率比较观察[J]. 中国老年保健医学, 2016, 14(6):85-87 CHEN Shi, SONG Man. MRI and CT diagnosis of different pathological stages of prostate cancer patients’ accuracy rate comparison[J]. Chinese journal of geriatric care, 2016, 14(6):85-87
[3] LIAO Shu, SHEN Dinggang. A feature-based learning framework for accurate prostate localization in CT images[J]. IEEE transactions on image processing, 2012, 21(8):3546-3559.
[4] CHEN Siqi, LOVELOCK D M, RADKE R J. Segmenting the prostate and rectum in CT imagery using anatomical constraints[J]. Medical image analysis, 2011, 15(1):1-11.
[5] GAO Yaozong, LIAO Shu, SHEN Dinggang. Prostate segmentation by sparse representation based classification[J]. Medical physics, 2012, 39(10):6372-6387.
[6] SHI Jun, ZHENG Xiao, LI Yan, et al. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease[J]. IEEE journal of biomedical and health informatics, 2018, 22(1):173-183.
[7] CAI Yunliang, LANDIS M, LAIDLEY D T, et al. Multi-modal vertebrae recognition using transformed deep convolution network[J]. Computerized medical imaging and graphics, 2016, 51:11-19.
[8] HINTON G E, OSINDERO S, THE Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[9] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, 2012:1097-1105.
[10] KOREZ R, LIKAR B, PERNU? F, et al. Model-based segmentation of vertebral bodies from MR images with 3D CNNs[C]//Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens, Greece, 2016:433-441.
[11] XIE Yuanpu, ZHANG Zizhao, SAPKOTA M, et al. Spatial clockwork recurrent neural network for muscle perimysium segmentation[C]//Proceedings of the 19th International Conference on Medical Image Computing and Computer-assisted Intervention. Athens, Greece, 2016:185-193.
[12] BROSCH T, TANG L Y W, YOO Y, et al. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation[J]. IEEE transactions on medical imaging, 2016, 35(5):1229-1239.
[13] YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks?[J]. arXiv:1411.1792, 2014.
[14] SHI Yinghuan, YANG Wanqi, GAO Yang, et al. Does manual delineation only provide the side information in CT prostate segmentation?[C]//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec City, Canada, 2017:692-700.
[15] KOUSHIK J. Understanding convolutional neural networks[J]. arXiv:1605.09081, 2016.
[16] XU Bing, WANG Naiyan, CHEN Tianqi, et al. Empirical evaluation of rectified activations in convolutional network[J]. arXiv:1505:00853, 2015.
[17] ZEILER M D, TAYLOR G W, FERGUS R. Adaptive deconvolutional networks for mid and high level feature learning[C]//Proceedings of the 2011 International Conference on Computer Vision. Barcelona, Spain, 2011:2018-2025.
[18] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany, 2015:234-241.
[19] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[J]. arXiv:1502.03167, 2015.
[20] LIN Min, CHEN Qiang, YAN Shuicheng. Network in network[J]. arXiv:1312.4400, 2013.
[21] FRANCE N. MICCAI grand challenge:prostate MR image segmentation 2012[EB/OL].[2012-10-01]. https://promise12.grand-challenge.org/home/.
[22] JIA Yangqing, SHELHAMER E, DONAHUE J, et al. Caffe:convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd ACM International Conference on Multimedia. Florida, USA, 2014:675-678.
[23] KETKAR N. Stochastic gradient descent[M]//KETKAR N. Deep Learning with Python. Berkeley:Apress, 2017.
[24] TAHA A A, HANBURY A. Metrics for evaluating 3D medical image segmentation:analysis, selection, and tool[J]. BMC medical imaging, 2015, 15:29.
Similar References:

Memo

-

Last Update: 2018-12-25

Copyright © CAAI Transactions on Intelligent Systems