Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230400038-6.doi: 10.11896/jsjkx.230400038

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Improved vnet Model for 3D Liver CT Image Segmentation

YANG Shuqi1, HAN Junling1,2, KANG Xiaodong1, YANG Jingyi1, GUO Hongyang1, LI Bo3   

  1. 1 School of Medical Image,Tianjin Medical University,Tianjin 300202,China
    2 The First Central Clinical College of Tianjin Medical University,Tianjin 300190,China
    3 The Third Central Clinical College of Tianjin Medical University,Tianjin 300170,China
  • Published:2024-06-06
  • About author:YANG Shuqi,born in 2001.Her main research interests include medical image processing and so on.
    KANG Xiaodong,born in 1964,Ph.D,professor,is a member of CCF(No.11186S).His main research interests include medical image processing and medical information system integration.
  • Supported by:
    Beijing-Tianjin-Hebei Collaborative Innovation Project(17YEXTZC00020).

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Abstract: Segmentation of 3D medical images is an important step in radiotherapy planning.In clinical practice,computed tomography is widely used for 3D medical image segmentation of the liver and liver tumours.Due to the complex edge structure and texture features of the liver,liver segmentation is still a challenging task.To address this problem,an improved vnet model for accurate segmentation of 3D liver CT images is proposed.Firstly,the liver CT images are truncated and resampled with HU values to complete the preprocessing of the 3D dataset.Meanwhile,the convolution kernel in the vnet decoder and encoder is replaced with an SG module,which is a combination of depthwise convolution and pointwise convolution,to reduce the number of parameters in the network model.Comparative experiments with the vnet model show that the proposed method is generally superior in the evaluation of the liver segmentation dataset,with a Dice coefficient of 94.93%,an improvement of 3.49% over the vnet model,greatly reducing the number of parameters of the model,while the method also shows good robustness and achieves superior segmentation results on the MSD spleen segmentation dataset and COVID-19 dataset.

Key words: Image segmentation, Liver, vnet, CT, 3D

CLC Number: 

  • TP391
[1]LIU Z,SONG Y Q,SHENG V S,et al.Liver CT sequence segmentation based with improved U-Net and graph cut[J].Expert Systems with Applications,2019,126(15):54-63.
[2]MOGHBEL M,MASHOHOR S,MAHMUD R,et al.Review ofliver segmentation and computer assisted detection/diagnosis methods in computed tomography[J].Artificial Intelligence Review,2018,50(4):497-537.
[3]WANG L D,WANG J,HU C J,et al.Liver Segmentation by Using an Optimal Framework for CT Images[J].Chinese Journal of Computers,2016,39(7):1477-1489.
[4]LIU Z,ZHANG X L,SONG Y Q,et al.Combining improved U-Net and Morphsnakes for liver segmentation[J].Journal of Image and Graphics,2018,23(8):1254-1262.
[5]KANG X D.Medical Image Processing[M].Beijing:People’s Medical Publishing House,2009:99-100.
[6]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[7]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention.Cham:Springer,2015:234-241.
[8]TRAN S T,CHENG C H,LIU D G.A Multiple Layer U-Net,Un-Net,for Liver and Liver Tumor Segmentation in CT[J].IEEE Access,2020,9:3752-3764.
[9]ÇIÇEK Ö,ABDULKADIR A,LIENKAMP S S,et al.3D U-Net:learning dense volumetric segmentation from sparse annotation[C]//Medical Image Computing and Computer-Assisted Intervention(MICCAI 2016).Athens,Greece,2016:424-432.
[10]MILLETARI F,NAVAB N,AHMADI S A.V-net:Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision(3DV).2016:565-571.
[11]ZHANG L,ZHANG J M,SHEN P Y,et al.Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation[J].IEEE Transactions on Medical Imaging,2020,39(9):2782-2793.
[12]IMRAN A-A-Z,HATAMIZADEH A,ANANTH S P,et al.Automatic segmentation of pulmonary lobes using a progressive dense V-network[C]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Granada,Spain,2018:282-290.
[13]ZHANG L,ZHANG J M,SHEN P Y,et al.Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation[J].IEEE Transactions on Medical Imaging,2020,39(9):2782-2793.
[14]WANG T,YANG J,JI Z,et al.Probabilistic diffusion for interactive image segmentation[J].IEEE Transactions on Image Processing,2018,28(1):330-342.
[15]HU T,ZHU Y X,TIAN L,et al.Lightweight convolutionalneural network architecture for mobile platforms[J].Computer Engineering,2019,45(1):17-22.
[16]HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2017-11-20].https://arxiv.org/abs/1704.04861.
[17]SHEN H Y,WU Y.MSFA-NET-based liver CT image segmentation method[J].Journal of Frontiers of Computer Science and Technology,2023,17(3):646-656.
[18]MA J L,DENG Y Y,MA Z P.Review of the deep learningmethod of liver tumor CT image[J].Journal of Image andGraphics,2020,25(10):2024-2046.
[19]TAHA A A,HANBURY A.Metrics for evaluating 3D medical image segmentation:analysis,selection,and tool[J].BMC Me-dical Imaging,2015,15(1):1-28.
[20]YAN Q,WANG W W.Division of liver and liver tumors based on conditional energy confrontation[J].Computer Engineering and Applications,2021,57(11):179-184.
[21]QIN W,WU J,HAN F,et al.Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation[J].Physics in Medicine & Biology,2018,63(9):095017
[22]HAN X.Automatic Liver Lesion Segmentation Using A DeepConvolutional Neural Network Method[J].arXiv:1704.07239,2017.
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