Journal of Guangdong University of Technology ›› 2025, Vol. 42 ›› Issue (1): 42-50.doi: 10.12052/gdutxb.230167

• Smart Medical • Previous Articles    

Segmentation and 3D Reconstruction of Meniscus Circumferential Fibers in MicroCT Images

Wang Biao1, Zhong Yingchun1, Luo Weishi2, Zhu Shuang3, Zeng Pujun4   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Neurosurgery, Second People's Hospital of Guangdong Province, Guangzhou 510317, China;
    3. Department of Arthropathy, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China;
    4. Hunan Drug Inspection Center, Changsha 410000, China
  • Received:2023-10-23 Published:2025-01-14

HTML

20

PDF (PC)

100

Abstract: The circumferential fibers are the key areas of meniscus stress. The construction of three-dimensional microstructure of circumferential fibers is of great significance for the treatment of meniscus injury and the development of artificial meniscus. At present, the circumferential fibers of meniscus in micro computed tomography (MicroCT) images are segmented manually. Because of the complex microstructure of meniscus, manual segmentation has some problems such as low efficiency and inconsistent segmentation standards. To solve the problem of few sample images, an image amplification method is proposed based on the characteristics of MicroCT images. To solve the problem of large edge segmentation errors, an improved model based on TransUNet algorithm is proposed, image Relative Position Encoding (iRPE) is introduced, and the loss function is improved. The experimental results show that: (1) the improved model can accurately and completely segment the meniscus tissue, and the segmentation results can successfully complete the three-dimensional reconstruction of circumferential fibers. (2) The introduced iRPE algorithm improves the segmentation effect of model edge details, the improved loss function enables the model to better adapt to the situation of sample imbalance, and the proposed image amplification method solves the problem of insufficient data sets and comprehensively improves the performance of the model.The results show that the average precision of circumferential fiber segmentation is 98.66%. (3) In the three-dimensional model of circumferential fibers, it is found that fibers are divided into two parts, and a small amount of fibers are divided into three parts. The proposed method can segment the meniscus circumferential fibers in MicroCT images with high accuracy and efficiency, and can pave the way for the study of the force analysis of meniscus in three-dimensional space.

Key words: meniscus, circumferential fibers, three-dimensional reconstruction, MicroCT images, deep learning

CLC Number: 

  • R318，TP391
[1] ZIHNA G, TOPUZ B, GÜNAL G, et al. Preparation of hybrid meniscal constructs using hydrogels and acellular matrices [J]. Journal of Biomaterials Science, Polymer Edition, 2023, 34(5): 587-611.
[2] PRENDERGAST M E, HEO S J, MAUCK R L, et al. Suspension bath bioprinting and maturation of anisotropic meniscal constructs [J]. Biofabrication, 2023, 15(3): 035003.
[3] BILGEN B, JAYASURIYA C T, OWENS B D. Current concepts in meniscus tissue engineering and repair [J]. Advanced Healthcare Materials, 2018, 7(11): 1701407.
[4] 朱爽, 梁敏娟, 赖剑强. 基于Micro-CT技术的半月板微观结构分析和三维重建[J]. 现代医用影像学, 2021, 30(8): 1432-1434.
[5] KARJALAINEN V P, KESTILÄ I, FINNILÄ M A, et al. Quantitative three-dimensional collagen orientation analysis of human meniscus posterior horn in health and osteoarthritis using micro-computed tomography [J]. Osteoarthritis and Cartilage, 2021, 29(5): 762-772.
[6] PETERSEN W, TILLMANN B. Collagenous fibril texture of the human knee joint menisci [J]. Anatomy and Embryology, 1998, 197: 317-324.
[7] FOX A J S, BEDI A, RODEO S A. The basic science of human knee menisci: structure, composition, and function [J]. Sports Health, 2012, 4(4): 340-351.
[8] TACK A, MUKHOPADHYAY A, ZACHOW S. Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative [J]. Osteoarthritis and Cartilage, 2018, 26(5): 680-688.
[9] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation [J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
[10] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[EB/OL]. arXiv: 1505.04597 (2015-05-18) [2023-09-15]. https://doi.org/10.48550/arXiv.1505.04597.
[11] STEGMANN H, WERKMEISTER R M, PFISTER M, et al. Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus [J]. Biomedical Optics Express, 2020, 11(3): 1539-1554.
[12] CHEN J H, LU Y Y, YU Q H, et al. TransUNet: Transformers make strong encoders for medical image segmentation[EB/OL]. arXiv: 2102.04346 (2021-02-08) [2023-09-15]. https://doi.org/10.48550/arXiv.2102.04306.
[13] FEDOROV A, BEICHEL R, KALPATHY-CRAMER J, et al. 3D Slicer as an image computing platform for the quantitative imaging network [J]. Magnetic Resonance Imaging, 2012, 30(9): 1323-1341.
[14] ZHU S, GE T, XIANG J P, et al. Microstructure analysis and reconstruction of a meniscus [J]. Orthopaedic Surgery, 2021, 13(1): 306-313.
[15] RUSSELL B C, TORRALBA A, MURPHT K P, et al. LabelMe: a database and web-based tool for image annotation [J]. International Journal of Computer Vision, 2008, 77: 157-173.
[16] WOODMASS J M, LAPRADE R F, SGAGLIONE N A, et al. Meniscal repair: reconsidering indications, techniques, and biologic augmentation [J]. The Journal of Bone and Joint surgery. American Volume, 2017, 99(14): 1222-1231.SS.
[17] WU K, PENG H, CHEN M, et al. Rethinking and improving relative position encoding for vision transformer[EB/OL]. arXiv: 2107.14222 (2021-07-29) [2023-09-15]. https://doi.org/10.48550/arXiv.2107.14222.
[18] SHAW P, USZKOREIT J, VASWANI A. Self-attention with relative position representations[EB/OL]. arXiv: 1803.02155 (2018-04-12) [2023-09-15]. https://doi.org/10.48550/arXiv.1803.02155.
[19] MILLETARI F, NAVAB N, AHMADI S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[EB/OL]. arXiv: 1606.04797 (2016-06-15) [2023-09-15]. https://doi.org/10.48550/arXiv.1606.04797.
[20] ROTH H R, ODA H, ZHOU X, et al. An application of cascaded 3D fully convolutional networks for medical image segmentation [J]. Computerized Medical Imaging and Graphics, 2018, 66: 90-99.
[21] 孙思语, 钟映春, 郑海阳, 等. MicroCT图像中周围神经束轮廓的建模[J]. 计算机仿真, 2022, 39(1): 258-262.
SUN S Y, ZHONG Y C, ZHENG H Y, et al. Modeling of contour of peripheral fasciculi in MicroCT images [J]. Computer Simulation, 2022, 39(1): 258-262.
[22] GOMALAVALLI R, MUTTAN S, SAI P M V. Boundary detection of renal using contour segmentation [J]. International Journal of Biomedical Engineering and Technology, 2018, 28(1): 53-66.
[1] Wang Ying, Cai Shu-ting, Xiong Xiao-ming. A Hotspot Detector Based on Active Learning and Visual State Space Models [J]. Journal of Guangdong University of Technology, 2024, 41(06): 45-51.doi: 10.12052/gdutxb.230167
[2] Luo Cheng, Zhang Jun. Adaptive Sampling and Memory-augmented Compressed Sensing Algorithm Based on Deep Learning [J]. Journal of Guangdong University of Technology, 2024, 41(04): 114-121.doi: 10.12052/gdutxb.230167
[3] Liang Yu-chen, Cai Nian, Ouyang Wen-sheng, Xie Yi-ying, Wang Ping. CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information [J]. Journal of Guangdong University of Technology, 2024, 41(01): 27-33.doi: 10.12052/gdutxb.230167
[4] Wu Ju-hua, Li Jun-feng, Tao Lei. Prediction of Adverse Drug Reactions Based on Knowledge Graph Embedding and Deep Learning [J]. Journal of Guangdong University of Technology, 2024, 41(01): 19-26,40.doi: 10.12052/gdutxb.230167
[5] Wen Wen, Liu Ying, Cai Rui-chu, Hao Zhi-feng. Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction [J]. Journal of Guangdong University of Technology, 2023, 40(04): 1-8.doi: 10.12052/gdutxb.230167
[6] Zhong Geng-jun, Li Dong. A Channel-splited Based Dual-branch Block for 3D Point Cloud Processing [J]. Journal of Guangdong University of Technology, 2023, 40(04): 18-23.doi: 10.12052/gdutxb.230167
[7] Jin Yu-kai, Li Zhi-sheng, Ou Yao-chun, Zhang Hua-gang, Zeng Jiang-yi, Chen Bo-chao. Prediction and Comparative Study of PM2.5 Concentration Based on Multi-stage Clustering [J]. Journal of Guangdong University of Technology, 2023, 40(03): 17-24.doi: 10.12052/gdutxb.230167
[8] Liu Dong-ning, Wang Zi-qi, Zeng Yan-jiao, Wen Fu-yan, Wang Yang. Prediction Method of Gene Methylation Sites Based on LSTM with Compound Coding Characteristics [J]. Journal of Guangdong University of Technology, 2023, 40(01): 1-9.doi: 10.12052/gdutxb.230167
[9] Xu Wei-feng, Cai Shu-ting, Xiong Xiao-ming. Visual Inertial Odometry Based on Deep Features [J]. Journal of Guangdong University of Technology, 2023, 40(01): 56-60,76.doi: 10.12052/gdutxb.230167
[10] Liu Hong-wei, Lin Wei-zhen, Wen Zhan-ming, Chen Yan-jun, Yi Min-qi. A MABM-based Model for Identifying Consumers' Sentiment Polarity―Taking Movie Reviews as an Example [J]. Journal of Guangdong University of Technology, 2022, 39(06): 1-9.doi: 10.12052/gdutxb.230167
[11] Zhang Yun, Wang Xiao-dong. A Review and Thinking of Deep Learning with a Restricted Number of Samples [J]. Journal of Guangdong University of Technology, 2022, 39(05): 1-8.doi: 10.12052/gdutxb.230167
[12] Zheng Jia-bi, Yang Zhen-guo, Liu Wen-yin. Marketing-Effect Estimation Based on Fine-grained Confounder Balancing [J]. Journal of Guangdong University of Technology, 2022, 39(02): 55-61.doi: 10.12052/gdutxb.230167
[13] Gary Yen, Li Bo, Xie Sheng-li. An Evolutionary Optimization of LSTM for Model Recovery of Geophysical Fluid Dynamics [J]. Journal of Guangdong University of Technology, 2021, 38(06): 1-8.doi: 10.12052/gdutxb.230167
[14] Lai Jun, Liu Zhen-yu, Liu Sheng-hai. A Small Sample Data Prediction Method Based on Global Data Shuffling [J]. Journal of Guangdong University of Technology, 2021, 38(03): 17-21.doi: 10.12052/gdutxb.230167
[15] Cen Shi-jie, He Yuan-lie, Chen Xiao-cong. A Monocular Depth Estimation Combined with Attention and Unsupervised Deep Learning [J]. Journal of Guangdong University of Technology, 2020, 37(04): 35-41.doi: 10.12052/gdutxb.230167
Viewed
Full text
100
HTML PDF
Just accepted Online first Issue Just accepted Online first Issue
0 0 0 0 2 98

  From Others local
  Times 15 85
  Rate 15% 85%

Abstract
102
Just accepted Online first Issue
0 10 92
  From local
  Times 102
  Rate 100%

Cited
Web of Science  Crossref   ScienceDirect  Search for Citations in Google Scholar >>
 
This page requires you have already subscribed to WoS.
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech