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A Feasibility Study of Knee Joint Semantic Segmentation on 3D MR Images

SHEN Le LU Qian TANG Hu WU Sha YI Yi SUN Yunda QIU Qian ZHANG Li ZHENG Zhuozhao CAI Xu

沈乐, 卢倩, 唐虎, 等. 膝关节三维磁共振影像语义分割的可行性研究[J]. CT理论与应用研究, 2022, 31(5): 531-542. DOI: 10.15953/j.ctta.2022.091. (英)
引用本文: 沈乐, 卢倩, 唐虎, 等. 膝关节三维磁共振影像语义分割的可行性研究[J]. CT理论与应用研究, 2022, 31(5): 531-542. DOI: 10.15953/j.ctta.2022.091. (英)
SHEN L, LU Q, TANG H, et al. A feasibility study of knee joint semantic segmentation on 3D MR images[J]. CT Theory and Applications, 2022, 31(5): 531-542. DOI: 10.15953/j.ctta.2022.091
Citation: SHEN L, LU Q, TANG H, et al. A feasibility study of knee joint semantic segmentation on 3D MR images[J]. CT Theory and Applications, 2022, 31(5): 531-542. DOI: 10.15953/j.ctta.2022.091


doi: 10.15953/j.ctta.2022.091
  • 中图分类号: O  242;TP  391.4

A Feasibility Study of Knee Joint Semantic Segmentation on 3D MR Images

  • 摘要: MR三维图像的全膝自动分割对膝骨关节炎疾病的诊断、指导和治疗具有重要意义。然而,膝关节的三维MR图像中涉及多种多样的解剖结构,人工勾画费时耗力;全膝自动分割不但节省人力,且可以通过更准确的细节勾画来提高关节炎的诊疗质量。现有的膝关节分割方法只关注众多解剖结构中的一个或几个结构,无法提供全膝分割的结果。本文研究基于三维神经网络的全膝分割方法,并致力于应对以下挑战:①在三维 MR图像上对包括骨骼和软组织在内的全膝15个解剖结构进行端到端分割;②前交叉韧带等小结构的鲁棒分割,前交叉韧带仅占全膝体积的0.036% 左右。在基于脂肪抑制三维各向同性中等权重VISTA序列的膝关节MR图像上,验证本文方法的平均分割精度为92.92%,其中9种结构的Dice相似系数在94% 以上,5种结构在87%~90% 之间,剩余1种结构在76% 左右。


  • Figure  1.  Histogram of voxels per class. The background occupies about 88.62%, the femur, tibia, cartilage and fibula occupy a higher percentage of voxels, while other soft tissues occupy few percentages of voxels. The anterior cruciate ligament only accounts for 0.036%. The anatomical structures of the whole knee vary in volume greatly

    Figure  2.  Overview of the SegResNet network architecture. The encoder network down-sample four times, and the residual block amounts of each stage are 1, 2, 2, 2, 4

    Figure  3.  (a) Whole volume inference using scan window, (b) Filter kernel of average smoothing strategy, (c) Filter kernel of Gaussian smoothing strategy, (d) A new filter kernel and smoothing strategy designed to eliminate artifacts and speed up inference. All kernels of 2D cases are shown for convenience

    Figure  4.  Performance of models

    Figure  5.  Performance on different anatomical structures

    Figure  6.  Model performance on test dataset

    Figure  7.  Performance comparison on normal and abnormal data

    Figure  8.  Segmentation results. From left to right are: bone, quadriceps tendon and patellar tendon, collateral ligaments, cartilage, meniscus & cruciate ligaments, and whole segmented knee joint

    Table  1.   Comparison of different inference strategies

    Average smoothingGaussian smoothing[22]Our method
    Inference time/s3612638
    Prediction of tibia
    下载: 导出CSV

    Table  2.   Comparison of kernel parameters

    $ s $/M$ \sigma $/MDSC
    下载: 导出CSV
  • [1] GUPTA S, HAWKER G A, LAPORTE A, et al. The economic burden of disabling hip and knee osteoarthritis (OA) from the perspective of individuals living with this condition[J]. Rheumatology (Oxford), 2005, 44(12): 1531−1537. DOI: 10.1093/rheumatology/kei049.
    [2] NIEMINEN M T, CASULA V, NEVALAINEN M T, et al. Osteoarthritis year in review 2018: Imaging[J]. Osteoarthritis anc Cartilage, 2019, 27(3): 401-411. DOI: 10.1016/j.joca.2018.12.009.
    [3] ECKSTEIN F, WIRTH W, CULVENOR A G. Osteoarthritis year in review 2020: Imaging[J]. Osteoarthritis and Cartilage, 2021, 29(2): 170−179. DOI: 10.1016/j.joca.2020.12.019.
    [4] ECKSTEIN F, CICUTTINI F, RAYNAULD J P, et al. Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): Morphological assessment[J]. Osteoarthritis and Cartilage, 2006, 14(A): 46−75. DOI: 10.1016/j.joca.2006.02.026.
    [5] BARR C, BAUER J, MALFAIR, D, et al. MR imaging of the ankle at 3 Tesla and 1.5 Tesla: Protocol optimization and application to cartilage, ligament and tendon pathology in cadaver specimens[J]. European Radiology, 2007, 17: 1518−1528. doi: 10.1007/s00330-006-0446-4
    [6] RAYNAULD J P, MARTEL-PELLETIER J, BERTHIAUME M J, et al. Long term evaluation of disease progression through the quantitative magnetic resonance imaging of symptomatic knee osteoarthritis patients: Correlation with clinical symptoms and radiographic changes[J]. Arthritis Research and Therapy, 2006, 8(1): R21.
    [7] HUNTER D J, MARCH L, SAMBROOK P N. The association of cartilage volume with knee pain[J]. Osteoarthritis and Cartilage, 2003, 11(10): 725−729. doi: 10.1016/S1063-4584(03)00160-2
    [8] ENGLUND M, GUERMAZI A, LOHMANDER L S. The meniscus in knee osteoarthritis[J]. Rheumatic Diseases Clinics of North America, 2009, 35(3): 579−590. doi: 10.1016/j.rdc.2009.08.004
    [9] LI R T, LORENZ S, XU Y, et al. Predictors of radiographic knee osteoarthritis after anterior cruciate ligament reconstruction[J]. The American Journal of Sports Medicine, 2011, 39(12): 2595−2603. doi: 10.1177/0363546511424720
    [10] SHARMA L, DUNLOP D, CAHUE S, et al. Quadriceps strength and osteoarthritis progression in malaligned and lax knees[J]. Annals of Internal Medicine, 2003, 138(8): 613−619. doi: 10.7326/0003-4819-138-8-200304150-00006
    [11] NORMAN B, PEDOIA V, MAJUMDAR S. Use of 2D U-net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry[J]. Radiology, 2018, 288(1): 177−185. DOI: 10.1148/radiol.2018172322.
    [12] PRASOON A, PETERSEN K, IGEL C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[C]//Medical Image Computing and Computer-Assisted Intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013, 16: 246-253.
    [13] GAJ S, YANG M, NAKAMURA K, et al. Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks[J]. Magnetic Resonance in Medicine, 2020, 84(1): 437−449. doi: 10.1002/mrm.28111
    [14] ZHOU Z, ZHAO G, KIJOWSKI R, et al. Deep convolutional neural network for segmentation of knee joint anatomy[J]. Magnetic Resonance in Medicine, 2018, 80(6): 2759−2770. doi: 10.1002/mrm.27229
    [15] MYRONENKO A. 3D MRI brain tumor segmentation using autoencoder regularization[J]. Springer International Publishing, 2019.
    [16] WU Y X, HE K M. Group normalization[J]. International Journal of Computer Vision, 2020, 128: 742−755. doi: 10.1007/s11263-019-01198-w
    [17] TANG P, ZU C, HONG M, et al. DA-DSUnet: Dual attention-based dense SU-net for automatic head-and-neck tumor segmentation in MRI images[J]. Neurocomputing, 2021, 435: 103−113. doi: 10.1016/j.neucom.2020.12.085
    [18] ZHU W, HUANG Y, ZENG, L, et al. AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy[J]. Medical Physics, 2018, 46(4): 576−589.
    [19] SALEH S S, ERDOGMUS D, GHOLIPOUR A. Tversky loss function for image segmentation using 3 D fully convolutional deep networks[J]. Machine Learning in Medical Imaging, 2017: 379−387.
    [20] CHEN X. WILLIAMS B M, VALLABHANENI S R, et al. Learning active contour models for medical image segmentation[C]//2019 Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 11624-11632.
    [21] LIN T, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017: 2999-3007.
    [22] ISENSEE F, JAEGER P F, KOHL S A, et al. Automated design of deep learning methods for biomedical image segmentation[J]. arXiv: 1904.08128, 2019.
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  • 被引次数: 0
  • 收稿日期:  2022-05-19
  • 修回日期:  2022-06-25
  • 录用日期:  2022-07-04
  • 网络出版日期:  2022-07-20
  • 刊出日期:  2022-10-01