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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Abstract Full text Figures/Tables Video References Cited by

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.	陳金東. 中國各類癌癥的發病率和死亡率現狀及發展趨勢. 遵義醫學院學報, 2018, 41(6): 653-662.
3.	魯欣, 蔣棟銘, 胡明, 等. 2004-2018年全國前列腺癌死亡率的流行特征及時間趨勢. 上海預防醫學, 2021, 33(10): 899-904.
4.	劉宗超, 李哲軒, 張陽, 等. 2020全球癌癥統計報告解讀. 腫瘤綜合治療電子雜志, 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, 魏強, 等. 前列腺癌的治療研究進展. 華西醫學, 2017, 32(2): 277-281.
7.	中國抗癌協會泌尿男生殖系統腫瘤專業委員會前列腺癌學組. 前列腺癌篩查中國專家共識(2021年版). 中國癌癥雜志, 2021, 31(5): 435-440.
8.	郭吉鋒, 紀志英, 解丙坤, 等. T2WI 聯合 DWI 及 DCE 對外周帶慢性前列腺炎與前列腺癌的診斷效能分析. 磁共振成像, 2020, 11(12): 1182-1185.
9.	劉可文, 劉紫龍, 汪香玉, 等. 基于級聯卷積神經網絡的前列腺磁共振圖像分類. 波譜學雜志, 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.	張永德, 彭景春, 劉罡, 等. 基于水平集的前列腺磁共振圖像分割方法研究. 儀器儀表學報, 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.	褚晶輝, 李曉川, 張佳祺, 等. 一種基于級聯卷積網絡的三維腦腫瘤精細分割. 激光與光電子學進展, 2019, 56(10): 75-84.
22.	張佳. 肺結節分割的V-Net模型改進研究. 上海: 東華大學,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. 陳金東. 中國各類癌癥的發病率和死亡率現狀及發展趨勢. 遵義醫學院學報, 2018, 41(6): 653-662.
3. 魯欣, 蔣棟銘, 胡明, 等. 2004-2018年全國前列腺癌死亡率的流行特征及時間趨勢. 上海預防醫學, 2021, 33(10): 899-904.
4. 劉宗超, 李哲軒, 張陽, 等. 2020全球癌癥統計報告解讀. 腫瘤綜合治療電子雜志, 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, 魏強, 等. 前列腺癌的治療研究進展. 華西醫學, 2017, 32(2): 277-281.
7. 中國抗癌協會泌尿男生殖系統腫瘤專業委員會前列腺癌學組. 前列腺癌篩查中國專家共識(2021年版). 中國癌癥雜志, 2021, 31(5): 435-440.
8. 郭吉鋒, 紀志英, 解丙坤, 等. T2WI 聯合 DWI 及 DCE 對外周帶慢性前列腺炎與前列腺癌的診斷效能分析. 磁共振成像, 2020, 11(12): 1182-1185.
9. 劉可文, 劉紫龍, 汪香玉, 等. 基于級聯卷積神經網絡的前列腺磁共振圖像分類. 波譜學雜志, 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. 張永德, 彭景春, 劉罡, 等. 基于水平集的前列腺磁共振圖像分割方法研究. 儀器儀表學報, 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. 褚晶輝, 李曉川, 張佳祺, 等. 一種基于級聯卷積網絡的三維腦腫瘤精細分割. 激光與光電子學進展, 2019, 56(10): 75-84.
22. 張佳. 肺結節分割的V-Net模型改進研究. 上海: 東華大學,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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