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基于监督学习的前列腺MR/TRUS图像分割和配准方法

丛明 吴童 刘冬 杨德勇 杜宇

丛明, 吴童, 刘冬, 杨德勇, 杜宇. 基于监督学习的前列腺MR/TRUS图像分割和配准方法[J]. 工程科学学报, 2020, 42(10): 1362-1371. doi: 10.13374/j.issn2095-9389.2019.10.10.006
引用本文: 丛明, 吴童, 刘冬, 杨德勇, 杜宇. 基于监督学习的前列腺MR/TRUS图像分割和配准方法[J]. 工程科学学报, 2020, 42(10): 1362-1371. doi: 10.13374/j.issn2095-9389.2019.10.10.006
CONG Ming, WU Tong, LIU Dong, YANG De-yong, DU Yu. Prostate MR/TRUS image segmentation and registration methods based on supervised learning[J]. Chinese Journal of Engineering, 2020, 42(10): 1362-1371. doi: 10.13374/j.issn2095-9389.2019.10.10.006
Citation: CONG Ming, WU Tong, LIU Dong, YANG De-yong, DU Yu. Prostate MR/TRUS image segmentation and registration methods based on supervised learning[J]. Chinese Journal of Engineering, 2020, 42(10): 1362-1371. doi: 10.13374/j.issn2095-9389.2019.10.10.006

基于监督学习的前列腺MR/TRUS图像分割和配准方法

doi: 10.13374/j.issn2095-9389.2019.10.10.006
基金项目: 国家自然科学基金资助项目(51575078, 51705063)
详细信息
    通讯作者:

    E-mail:liud@dlut.edu.cn

  • 中图分类号: TP391.7

Prostate MR/TRUS image segmentation and registration methods based on supervised learning

More Information
  • 摘要: 前列腺核磁超声图像配准融合有助于实现前列腺肿瘤的靶向穿刺。传统的配准方法主要是针对手动分割的前列腺核磁(Magnetic resonance, MR)和经直肠超声(Trans-rectal ultrasound, TRUS)图像上对应的生理特征点作为参考点,进行刚体或非刚体配准。针对超声图像因成像质量低导致手动分割配准效率低下的问题,提出一种基于监督学习的前列腺MR/TRUS图像自动分割方法,与术前核磁图像进行非刚体配准。首先,针对图像分割任务训练前列腺超声图像的活动表观模型(Active appearance model, AAM),并基于随机森林建立边界驱动的数学模型,实现超声图像自动分割。接着,提取术前分割的核磁图像与自动分割的超声图像建立轮廓的形状特征矢量,进行特征匹配与图像配准。实验结果表明,本文方法能准确实现前列腺超声图像自动分割与配准融合,9组配准结果的戴斯相似性系数(Dice similarity coefficient, DSC)均大于0.98,同时尿道口处特征点的平均定位精度达1.64 mm,相比传统方法具有更高的配准精度。
  • 图  1  初始姿态${{{T}}_{{\rm{ini}}}}$对收敛结果的影响。(a~d)初始姿态参数过大无法收敛;(e~f)初始姿态满足收敛条件

    Figure  1.  Effect of the initial position parameter ${{{T}}_{{\rm{ini}}}}$ on the results of convergence: (a−d) large initial position parameter resulted error convergence; (e−f) initial position parameter met convergence conditions

    图  2  前列腺随机森林模型的训练和预测过程

    Figure  2.  Training and prediction of the prostate random forest model

    图  3  均值形状的初始姿态

    Figure  3.  Initial position parameters of the mean shape model

    图  4  姿态${{{T}}_k}$下的坐标变化关系

    Figure  4.  Coordinate transformation relationship at position ${{{T}}_k}$

    图  5  生成最小外接矩形

    Figure  5.  Generation of the minimum enclosing rectangle

    图  6  前列腺超声图像的自动分割过程。(a)前列腺TRUS图像;(b)参数寻优结果;(c)图像分割结果;(d)分割对比结果

    Figure  6.  Automatic segmentation process of prostate TRUS images: (a) prostate TRUS image; (b) parameters optimization result; (c) segmentation result; (d) image segmentation comparison results

    图  7  待配准的前列腺MR/TRUS图像。(a)MR图像轮廓点;(b)TRUS图像轮廓点

    Figure  7.  Prostate MR/TRUS images to be registered: (a) contour points on MR image; (b) contour points on TRUS image

    图  8  形状描述符的建立

    Figure  8.  Construction of the shape descriptor

    图  9  改进KM算法的对比结果

    Figure  9.  Results compared with the improved KM algorithm

    图  10  图像配准融合结果。(a)MR图像的变换结果;(b)MR/TRUS图像融合结果

    Figure  10.  Registration results: (a) transformation result of MR image; (b) registration result of MR/TRUS images

    图  11  前列腺超声图像分割结果。(a1~a5)待分割的TRUS图像;(b1~b5)随机森林预分割结果;(c1c5)轮廓分割收敛过程

    Figure  11.  Segmentation results of prostate US images: (a1‒a5) initial TRUS images; (b1‒b5) pre-segmentation results of random forest; (c1‒c5) convergence processes of contour segmentation

    图  12  核磁超声图像配准结果对比

    Figure  12.  Results of MR/TRUS images registration

    图  13  本文方法与传统方法对比结果

    Figure  13.  Results compared with the traditional method

    图  14  尿道特征点定位结果

    Figure  14.  Location results of urethral points

    表  1  尿道特征点定位结果对比

    Table  1.   Comparison of location results of urethral points

    SampleMethod proposed Literature method
    DSCTE/mm DSCTE/mm
    10.99381.76 0.99391.93
    20.98731.24 0.98163.84
    30.98971.92 0.98931.27
    40.99061.58 0.98722.55
    50.98711.47 0.98841.42
    AP0.98971.59 0.98802.20
    ${d_2}$0.00240.23 0.00390.93
    下载: 导出CSV
  • [1] 邓益森, 何宇辉, 周晓峰. 前列腺靶向穿刺技术发展概况. 微创泌尿外科杂志, 2018, 7(6):428

    Deng Y S, He Y H, Zhou X F. Development of prostate targeted puncture technology. J Mimimally Invasive Urology, 2018, 7(6): 428
    [2] Guichard G, Larré, Gallina A, et al. Extended 21-sample needle biopsy protocol for diagnosis of prostate cancer in 1000 consecutive patients. Eur Urol, 2007, 52(2): 430 doi: 10.1016/j.eururo.2007.02.062
    [3] 周智恩, 严维刚, 周毅, 等. MRI-超声融合引导下前列腺靶向穿刺活检的最新进展. 中华外科杂志, 2016, 54(10):792 doi: 10.3760/cma.j.issn.0529-5815.2016.10.016

    Zhou Z E, Yan W G, Zhou Y, et al. Recent progress in MRI-ultrasound fusion for guidance of targeted prostate biopsy. Chin J Surg, 2016, 54(10): 792 doi: 10.3760/cma.j.issn.0529-5815.2016.10.016
    [4] 曲华伟, 刘辉, 崔子连, 等. 重点穿刺MRI可疑病灶区域在MRI/TRUS融合成像引导靶向前列腺穿刺中的诊断价值. 中华男科学杂志, 2016, 22(9):782

    Qu H W, Liu H, Cui Z L, et al. Focusing on MRI-suspected lesions in targeted transrectal prostate biopsy guided by MRI-TRUS fusion imaging for the diagnosis of prostate cancer. Natl J Androl, 2016, 22(9): 782
    [5] Schlenker B, Apfelbeck M, Buchner A, et al. MRI-TRUS fusion biopsy of the prostate: quality of image fusion in a clinical setting. Clin Hemorheol Microcirculat, 2018, 70(4): 433
    [6] Mitra J, Martí R, Oliver A, et al. Prostate multimodality image registration based on B-splines and quadrature local energy. Int J Comput Assisted Radiol Surg, 2012, 7(3): 445 doi: 10.1007/s11548-011-0635-8
    [7] Sun Y, Yuan J, Qiu W, et al. Three-dimensional nonrigid MR-TRUS registration using dual optimization. IEEE Trans Med Imag, 2015, 34(5): 1085 doi: 10.1109/TMI.2014.2375207
    [8] Moradi M, Janoos F, Fedorov A, et al. Two solutions for registration of ultrasound to MRI for image-guided prostate interventions // 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, 2012: 1129
    [9] Fedorov A, Khallaghi S, Sánchez C A, et al. Open-source image registration for MRI-TRUS fusion-guided prostate interventions. Int J Comput Assisted Radiol Surg, 2015, 10(6): 925 doi: 10.1007/s11548-015-1180-7
    [10] 倪东, 吴海浪. 基于核磁-超声融合的前列腺靶向穿刺系统. 深圳大学学报: 理工版, 2016, 33(2):111 doi: 10.3724/SP.J.1249.2016.02111

    Ni D, Wu H L. MRI-TRUS multi-modality image fusion for targeted prostate biopsy. J Shenzhen Univ Sci Eng, 2016, 33(2): 111 doi: 10.3724/SP.J.1249.2016.02111
    [11] 王炜荣. MR与TRUS图像辅助前列腺穿刺技术研究[学位论文]. 哈尔滨: 哈尔滨工业大学, 2018

    Wang W R. Research on Prostate Puncture Assisted by MR and TRUS Image[Dissertation]. Harbin: Harbin Institute of Technology, 2018
    [12] 杜超. 前列腺穿刺引导中的MR和TRUS图像去噪与分割方法[学位论文]. 哈尔滨: 哈尔滨工业大学, 2019

    Du C. MR and TRUS Image Denoising and Segmentation Methods in Prostate Puncture Guidance[Dissertation]. Harbin: Harbin Institute of Technology, 2019
    [13] Cootes T F, Edwards G J, Taylor C J. Active appearance models. IEEE Trans Pattern Anal Mach Intellig, 2001, 23(6): 681 doi: 10.1109/34.927467
    [14] Bookstein F L. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intellig, 1989, 11(6): 567 doi: 10.1109/34.24792
    [15] Svetnik V, Liaw A, Tong C, et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci, 2003, 43(6): 1947 doi: 10.1021/ci034160g
    [16] Rohr K, Fornefett M, Stiehl H S. Spline-based elastic image registration: integration of landmark errors and orientation attributes. Comput Vision Image Understand, 2003, 90(2): 153 doi: 10.1016/S1077-3142(03)00048-1
    [17] Evangelidis G D, Psarakis E Z. Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans Pattern Anal Mach Intellig, 2008, 30(10): 1858 doi: 10.1109/TPAMI.2008.113
    [18] Ghosh P, Mitchell M, Tanyi J A, et al. Incorporating priors for medical image segmentation using a genetic algorithm. Neurocomputing, 2016, 195: 181 doi: 10.1016/j.neucom.2015.09.123
    [19] Cosío F A. Automatic initialization of an active shape model of the prostate. Med Image Anal, 2008, 12(4): 469 doi: 10.1016/j.media.2008.02.001
    [20] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intellig, 2002, 24(4): 509 doi: 10.1109/34.993558
    [21] Munkres J. Algorithms for the assignment and transportation problems. J Soc Ind Appl Math, 1957, 5(1): 32 doi: 10.1137/0105003
    [22] Šerifović-Trbalić A, Demirović D, Prljača N, et al. Intensity-based elastic registration incorporating anisotropic landmark errors and rotational information. Int J Comput Assisted Radiol Surg, 2009, 4(5): 463 doi: 10.1007/s11548-009-0358-2
    [23] Keys R. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process, 1981, 29(6): 1153 doi: 10.1109/TASSP.1981.1163711
    [24] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intellig, 1993, 15(9): 850 doi: 10.1109/34.232073
    [25] Dice L R. Measures of the amount of ecologic association between species. J Ecol, 1945, 26(3): 297 doi: 10.2307/1932409
    [26] Mitra J, Marti R, Oliver A, et al. A comparison of thin-plate splines with automatic correspondences and B-splines with uniform grids for multimodal prostate registration. Proc SPIE - Int Soc Opt Eng, 2011, 7964(2): 150
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  • 收稿日期:  2019-10-10
  • 刊出日期:  2020-10-25

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