Abstract
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27–44%) or over-diagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difficult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively.
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References
American Cancer Society. Key statistics for prostate cancer: Prostate cancer facts. https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html. Accessed 17 Oct 2021
AstraZeneca, A personalized approach in prostate cancer (2020). https://www.astrazeneca.com/our-therapy-areas/oncology/prostate-cancer.html. Accessed 17 Oct 2021
Chen, M., et al.: Prostate cancer detection: comparison of t2-weighted imaging, diffusion-weighted imaging, proton magnetic resonance spectroscopic imaging, and the three techniques combined. Acta Radiologica 49(5), 602–610 (2008)
Haralick, R., Shapiro, L.: Image segmentation techniques. Comput. Vision Graph. Image Process. 29(1), 100–132 (1985)
Aldoj, N., Biavati, F., Michallek, F., Stober, S., Dewey, M.: Automatic prostate and prostate zones segmentation of magnetic resonance images using densenet-like u-net. Sci. Rep. 10, 08 (2020)
Rasch, C.R.N., et al.: Human-computer interaction in radiotherapy target volume delineation: a prospective, multi-institutional comparison of user input devices. J. Digital Imaging 24(5), 794–803 (2011)
Mahapatra, D., Buhmann, J.-M.: Prostate mri segmentation using learned semantic knowledge and graph cuts. IEEE Trans. Biomed. Eng. 61(3), 756–764 (2014)
Elguindi, S., et al.: Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Phys. Imaging Radiat. Oncol. 12, 80–86 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Yufeng, W., Jiachen, W., Jin, S., Cao, L., Jin, G.: Dense-u-net: dense encoder-decoder network for holographic imaging of 3D particle fields. Optics Commun. 493, 126970 (2021)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas (2018)
Rodríguez, J., Ochoa-Ruiz, G., Mata, C.: A prostate mri segmentation tool based on active contour models using a gradient vector flow. Appl. Sci. 10(18), 6163 (2020)
Sun, Y.: Multiparametric mri and radiomics in prostate cancer: a review. Aust. Phys. Eng. Sci. Med. 42(1), 3–25 (2019)
Gupta, R., Kauffman, C., Polascik, T., Taneja, S., Rosenkrantz, A.: The state of prostate mri in 2013. Oncology (Williston Park) 27(4), 262–70 (2013)
Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys./Assoc. Med. Phys. India 35(1), 3 (2010)
Li, H., Lee, C.H., Chia, D., Lin, Z., Huang, W., Tan, C.H.: Machine learning in prostate MRI for prostate cancer: current status and future opportunities. Diagnostics 12(2), 289 (2022)
Klein, S., van der Heide, U.A., Raaymakers, B.W., Kotte, A.N.T.J., Staring, M., Pluim, J.P.W.: Segmentation of the prostate in mr images by atlas matching. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1300–1303 (2007)
Reda, I., Elmogy, M., Aboulfotouh, A., Ismail, M., El-Baz, A., Keynton, R.: Prostate segmentation using deformable model-based methods. Biomed. Image Seg. Adv. Trends 293, 15–40 (2016)
Liu, X., Haider, M.A., Yetik, I.S.: Unsupervised 3D prostate segmentation based on diffusion-weighted imaging mri using active contour models with a shape prior. In: JECE 2011 (2011)
Comelli, A., et al.: Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Appl. Sci. 11(2), 782 (2021)
Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19(1), 221–248 (2017). PMID: 28301734
Zhu, Q., Du, B., Turkbey, B., Choyke, P.L., Yan, P.: Deeply-supervised CNN for prostate segmentation. CoRR, abs/1703.07523 (2017)
Zabihollahy, F., Schieda, N., Jeyaraj, S.K., Ukwatta, E.: Automated segmentation of prostate zonal anatomy on t2-weighted (t2w) and apparent diffusion coefficient (adc) map mr images using u-nets. Med. Phys. 46(7), 3078–3090 (2019)
Clark, T., Wong, A., Haider, M., Khalvati, F.: Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted mr images, pp. 97–104 (2017)
Rundo, L., et al.: Use-net: incorporating squeeze-and-excitation blocks into u-net for prostate zonal segmentation of multi-institutional MRI datasets. CoRR, abs/1904.08254 (2019)
Rundo, L., et al.: Cnn-based prostate zonal segmentation on t2-weighted MR images: a cross-dataset study. CoRR, abs/1903.12571 (2019)
Li, S., Dong, M., Guangming, D., Xiaomin, M.: Attention dense-u-net for automatic breast mass segmentation in digital mammogram. IEEE Access 7, 59037–59047 (2019)
) Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., Fan, C.: Sa-unet: spatial attention u-net for retinal vessel segmentation. ArXiv, abs/2004.03696 (2020)
Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using Augmentor. Bioinformatics 35(21), 4522–4524 (2019)
Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. CoRR, abs/1802.06955 (2018)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Taha, A.-A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1–28 (2015)
Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021)
Sha, Y.: Keras-unet-collection (2021). https://github.com/yingkaisha/keras-unet-collection
Hyun, L.J.: Pytorch implementation of u-net, r2u-net, attention u-net, attention r2u-net (2019). https://github.com/LeeJunHyun/Image_Segmentation
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The authors wish to thank the AI Hub and the CIIOT at ITESM for their support for carrying the experiments reported in this paper in their NVIDIA’s DGX computer.
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Quihui-Rubio, P.C., Ochoa-Ruiz, G., Gonzalez-Mendoza, M., Rodriguez-Hernandez, G., Mata, C. (2022). Comparison of Automatic Prostate Zones Segmentation Models in MRI Images Using U-net-like Architectures. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13612. Springer, Cham. https://doi.org/10.1007/978-3-031-19493-1_23
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