Segmentation of prostate region in magnetic resonance images based on improved V-Net_Journal of Biomedical Engineering

Authors：

GAO Mingyuan ¹ ,  YAN Shiju ¹ , SONG Chengli ¹ , ZHU Zehua ² , XIE Erze ¹ , FANG Boya ¹

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Shanghai Tenth People’s Hospital, Shanghai 200072, P. R. China;

Corresponding author：

YAN Shiju, Email: yanshiju@usst.edu.cn

Keywords：

Magnetic resonance; Attention mechanism; V-Net; Image segmentation; Prostate

DOI：

10.7507/1001-5515.202202052

Video：

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Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.

Citation： GAO Mingyuan, YAN Shiju, SONG Chengli, ZHU Zehua, XIE Erze, FANG Boya. Segmentation of prostate region in magnetic resonance images based on improved V-Net. Journal of Biomedical Engineering, 2023, 40(2): 226-233. doi: 10.7507/1001-5515.202202052 Copy

1.	Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	陈金东. 中国各类癌症的发病率和死亡率现状及发展趋势. 遵义医学院学报, 2018, 41(6): 653-662.
3.	鲁欣, 蒋栋铭, 胡明, 等. 2004-2018年全国前列腺癌死亡率的流行特征及时间趋势. 上海预防医学, 2021, 33(10): 899-904.
4.	刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
5.	Zhu Y, Mo M, Wei Y, et al. Epidemiology and genomics of prostate cancer in Asian men. Nat Rev Urol, 2021, 18(5): 282-301.
6.	曹德宏, 柳良仁, 魏强, 等. 前列腺癌的治疗研究进展. 华西医学, 2017, 32(2): 277-281.
7.	中国抗癌协会泌尿男生殖系统肿瘤专业委员会前列腺癌学组. 前列腺癌筛查中国专家共识(2021年版). 中国癌症杂志, 2021, 31(5): 435-440.
8.	郭吉锋, 纪志英, 解丙坤, 等. T2WI 联合 DWI 及 DCE 对外周带慢性前列腺炎与前列腺癌的诊断效能分析. 磁共振成像, 2020, 11(12): 1182-1185.
9.	刘可文, 刘紫龙, 汪香玉, 等. 基于级联卷积神经网络的前列腺磁共振图像分类. 波谱学杂志, 2020, 37(2): 152-161.
10.	Vincent G, Guillard G, Bowes M. Fully automatic segmentation of the prostate using active appearance models. Prostate MR Image Segmentation, 2012, 2012: 2.
11.	Malmberg F, Strand R, Kullberg J, et al. Smart paint a new interactive segmentation method applied to MR prostate segmentation. Prostate MR Image Segmentation, 2012, 2012: 1.
12.	张永德, 彭景春, 刘罡, 等. 基于水平集的前列腺磁共振图像分割方法研究. 仪器仪表学报, 2017, 38(2): 416-424.
13.	Liu Q, Dou Q, Yu L, et al. MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE transactions on medical imaging, 2020, 39(9): 2713-2724.
14.	Cuocolo R, Comelli A, Stefano A, et al. Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. Journal of Magnetic Resonance Imaging, 2021, 54(2): 452-459.
15.	Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich: Springer Lncs, 2015: 234-241.
16.	Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston: IEEE, 2015: 3431-3440.
17.	Lei Y, Dong X, Tian Z, et al. CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network. Med Phys, 2020, 47(2): 530-540.
18.	Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
19.	Kohl S, Bonekamp D, Schlemmer H P, et al. Adversarial networks for the detection of aggressive prostate cancer. arXiv preprint, 2017, arXiv: 1702.08014.
20.	Litjens G, Toth R, van de Ven W, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal, 2014, 18(2): 359-373.
21.	褚晶辉, 李晓川, 张佳祺, 等. 一种基于级联卷积网络的三维脑肿瘤精细分割. 激光与光电子学进展, 2019, 56(10): 75-84.
22.	张佳. 肺结节分割的V-Net模型改进研究. 上海: 东华大学,2021.
23.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
24.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.
25.	Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift//International Conference On Machine Learning. Lille: PMLR, 2015: 448-456.
26.	Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2818-2826.
27.	Drozdzal M, Vorontsov E, Chartrand G, et al. The importance of skip connections in biomedical image segmentation//International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Munich: Springer Cham, 2016: 179-187.
28.	Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens: Springer International Publishing, 2016: 424-432.
29.	Ou Y, Doshi J, Erus G, et al. Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. Medical Image Computing and Computer Assisted Intervention (MICCAI) Grand Challenge: Prostate MR Image Segmentation, 2012, 7: 1-7.

1. Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 陈金东. 中国各类癌症的发病率和死亡率现状及发展趋势. 遵义医学院学报, 2018, 41(6): 653-662.
3. 鲁欣, 蒋栋铭, 胡明, 等. 2004-2018年全国前列腺癌死亡率的流行特征及时间趋势. 上海预防医学, 2021, 33(10): 899-904.
4. 刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
5. Zhu Y, Mo M, Wei Y, et al. Epidemiology and genomics of prostate cancer in Asian men. Nat Rev Urol, 2021, 18(5): 282-301.
6. 曹德宏, 柳良仁, 魏强, 等. 前列腺癌的治疗研究进展. 华西医学, 2017, 32(2): 277-281.
7. 中国抗癌协会泌尿男生殖系统肿瘤专业委员会前列腺癌学组. 前列腺癌筛查中国专家共识(2021年版). 中国癌症杂志, 2021, 31(5): 435-440.
8. 郭吉锋, 纪志英, 解丙坤, 等. T2WI 联合 DWI 及 DCE 对外周带慢性前列腺炎与前列腺癌的诊断效能分析. 磁共振成像, 2020, 11(12): 1182-1185.
9. 刘可文, 刘紫龙, 汪香玉, 等. 基于级联卷积神经网络的前列腺磁共振图像分类. 波谱学杂志, 2020, 37(2): 152-161.
10. Vincent G, Guillard G, Bowes M. Fully automatic segmentation of the prostate using active appearance models. Prostate MR Image Segmentation, 2012, 2012: 2.
11. Malmberg F, Strand R, Kullberg J, et al. Smart paint a new interactive segmentation method applied to MR prostate segmentation. Prostate MR Image Segmentation, 2012, 2012: 1.
12. 张永德, 彭景春, 刘罡, 等. 基于水平集的前列腺磁共振图像分割方法研究. 仪器仪表学报, 2017, 38(2): 416-424.
13. Liu Q, Dou Q, Yu L, et al. MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE transactions on medical imaging, 2020, 39(9): 2713-2724.
14. Cuocolo R, Comelli A, Stefano A, et al. Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. Journal of Magnetic Resonance Imaging, 2021, 54(2): 452-459.
15. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich: Springer Lncs, 2015: 234-241.
16. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston: IEEE, 2015: 3431-3440.
17. Lei Y, Dong X, Tian Z, et al. CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network. Med Phys, 2020, 47(2): 530-540.
18. Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
19. Kohl S, Bonekamp D, Schlemmer H P, et al. Adversarial networks for the detection of aggressive prostate cancer. arXiv preprint, 2017, arXiv: 1702.08014.
20. Litjens G, Toth R, van de Ven W, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal, 2014, 18(2): 359-373.
21. 褚晶辉, 李晓川, 张佳祺, 等. 一种基于级联卷积网络的三维脑肿瘤精细分割. 激光与光电子学进展, 2019, 56(10): 75-84.
22. 张佳. 肺结节分割的V-Net模型改进研究. 上海: 东华大学,2021.
23. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
24. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.
25. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift//International Conference On Machine Learning. Lille: PMLR, 2015: 448-456.
26. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2818-2826.
27. Drozdzal M, Vorontsov E, Chartrand G, et al. The importance of skip connections in biomedical image segmentation//International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Munich: Springer Cham, 2016: 179-187.
28. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens: Springer International Publishing, 2016: 424-432.
29. Ou Y, Doshi J, Erus G, et al. Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. Medical Image Computing and Computer Assisted Intervention (MICCAI) Grand Challenge: Prostate MR Image Segmentation, 2012, 7: 1-7.

Journal of Biomedical Engineering

Segmentation of prostate region in magnetic resonance images based on improved V-Net

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