Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (05): 41-46.doi: 10.12052/gdutxb.220197

• Computer Science and Technology • Previous Articles     Next Articles

Super-resolution Segmentation of Hepatobiliary Ducts Based on Deep Correlation Mechanism

Zheng Yu1, Cai Nian1, Ouyang Wen-sheng1, Xie Yi-ying1, Wang Ping2   

  1. 1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Hepatobiliary Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
  • Received:2022-12-27 Online:2023-09-25 Published:2023-09-26

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Abstract: Hepatobiliary stone disease is a common liver disease and has become the main cause of death from non-neoplastic biliary diseases in China, and it is important to achieve interpolated segmentation reconstruction between slices of hepatobiliary ducts. In this research, an end-to-end framework for super-resolution abdominal CT image processing is proposed based on deep correlation mechanism. The framework cascades an inter-slice interpolation network and a segmentation network, in which the ConvLSTM is introduced to enhance the extraction of high-dimensional feature information of hepatobiliary ducts between slices. A novel loss is designed by combining the loss of the interpolation network and the loss of the segmentation network. Experimental results show that the proposed framework is superior to the existing deep learning methods for the segmentation of hepatobiliary ducts, which is beneficial for the 3D reconstruction of hepatobiliary ducts.

Key words: hepatobiliary ducts, super-resolution segmentation, deep correlation mechanism, ConvLSTM

CLC Number: 

  • TP391
[1] PEREIRA-LIMA J C, JAKOBS R, WINTER U H, et al. Long-term results (7 to 10 years) of endoscopic papillotomy for choledocholithiasis. Multivariate analysis of prognostic factors for the recurrence of biliary symptoms [J]. Gastrointestinal Endoscopy, 1998, 48(5): 457-464.
[2] UENO N, OZAWA Y, AIZAWA T. Prognostic factors for recurrence of bile duct stones after endoscopic treatment by sphincter dilation [J]. Gastrointestinal Endoscopy, 2003, 58(3): 336-340.
[3] 李江, 刘斌. 肝胆管结石初始外科治疗的规范化研究[J]. 临床肝胆病杂志, 2015, 31(10): 1612-1614.
LI J, LIU B. More attention to initial surgical treatment of hepatolithiasis [J]. Journal of Clinical Hepatol, 2015, 31(10): 1612-1614.
[4] 王平, 刘成成, 陶海粟, 等. 经皮肝Ⅰ期胆道造瘘取石治疗有胆道手术史患者的肝内胆管结石[J]. 中华肝胆外科杂志, 2019, 25(2): 106-110.
WANG P, LIU C C, TAO H S, et al. Percutaneous transhepatic one-step biliary fistulation for patients with hepatolithiasis and hepatobiliary surgery history [J]. Chinese Journal of Hepatobiliary Surgery, 2019, 25(2): 106-110.
[5] NAKAGAWA Y, ROSENFELD A. Some experiments on variable thresholding [J]. Pattern Recognition, 1979, 11(3): 191-204.
[6] MORE M D, ANDURKAR G K. Edge detection techniques: a comparative approach [J]. World Journal of Science and Technology, 2012, 2(4): 142-145.
[7] ZENG Y, LIAO S, TANG P, et al. Automatic liver vessel segmentation using 3D region growing and hybrid active contour model [J]. Computers in Biology and Medicine, 2018, 97: 63-73.
[8] SHEN D, WU G, SUK H I. Deep learning in medical image analysis [J]. Annual Review of Biomedical Engineering, 2017, 19(5): 221-248.
[9] FU X, CAI N, HUANG K, et al. M-net: a novel U-net with multi-stream feature fusion and multi-scale dilated convolutions for bile ducts and hepatolith segmentation [J]. IEEE Access, 2019, 7: 148645-148657.
[10] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention. Munich: MICCAI, 2015: 234-241.
[11] 陈芝涛. 基于多尺度上下文特征学习的胆管及胆结石图像分割[J]. 现代计算机, 2021, 27(25): 74-78.
CHEN Z T. Bile ducts and hepatoliths segmentation via multi-scale context feature learning [J]. Modern Computer, 2021, 27(25): 74-78.
[12] KITRUNGROTSAKUL T, HAN X H, IWAMOTO Y, et al. VesselNet: a deep convolutional neural network with multi pathways for robust hepatic vessel segmentation [J]. Computerized Medical Imaging and Graphics, 2019, 75: 74-83.
[13] YAN Q, WANG B, ZHANG W, et al. Attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation [J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(7): 2629-2642.
[14] SU J, LIU Z, ZHANG J, et al. DV-Net: accurate liver vessel segmentation via dense connection model with D-BCE loss function [J]. Knowledge-Based Systems, 2021, 232: 107471.
[15] HAO W, ZHANG J, SU J, et al. HPM-Net: hierarchical progressive multiscale network for liver vessel segmentation in CT images [J]. Computer Methods and Programs in Biomedicine, 2022, 224: 107003.
[16] 杨柳, 李德玉, 何爱军, 等. 医学图像多维重建中的插值问题研究进展[J]. 生物医学工程学杂志, 2003(4): 728-732.
YANG L, LI D Y, HE A J, et al. A review of interpolation in multi-dimensional reconstruction of medical images [J]. Journal of Biomedical Engineering, 2003(4): 728-732.
[17] 闫芳. 医学影像重建算法研究及应用[D]. 济南: 山东师范大学, 2021.
[18] NIKLAUS S, MAI L, LIU F. Video frame interpolation via adaptive separable convolution[C]//IEEE/CVF International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 261-270.
[19] JIANG H, SUN D, JAMPANI V, et al. Super slomo: high quality estimation of multiple intermediate frames for video interpolation[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 9000-9008.
[20] LU L, WU R, LIN H, et al. Video frame interpolation with transformer[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 3532-3542.
[21] LUO Y, PAN J, TANG J. Bi-directional pseudo-three-dimensional network for video frame interpolation [J]. IEEE Transactions on Image Processing, 2022, 31: 6773-6788.
[22] OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention u-net: learning where to look for the pancreas[EB/OL]. (2018-05-20) [2022-12-26].https://doi.org/10.48550/arXiv.1804.03999.
[23] ZHOU Z, SIDDIQUEE M M R, TAJBAKHSH N, et al. Unet++: redesigning skip connections to exploit multiscale features in image segmentation [J]. IEEE Transactions on Medical Imaging, 2019, 39(6): 1856-1867.
[24] CAO H, WANG Y, CHEN J, et al. Swin-unet: unet-like pure transformer for medical image segmentation[EB/OL]. (2021-05-12) [2022-12-26]. https://doi.org/10.48550/arXiv.2105.05537.
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