Title

U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Affiliation

Les, Tomasz : Faculty of Electrical Engineering, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warszawa, Poland

Authors

Les, Tomasz

Keywords

kidney detection ; medical image processing ; U-Net ; frames partitioning ; volumetric analysis

Divisions of PAS

Nauki Techniczne

Coverage

e137051

Bibliography

1.  Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition”, Proc. IEEE 86(11), 2278‒2324 (1998), doi: 10.1109/5.726791.
2.  F. Isensee, “An attempt at beating the 3D U-Net”, ed. K.H. Maier-Hein, 2019.
3.  Ö. Çiçek, A. Abdulkadir, S.S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation”, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016), 424‒432, Springer International Publishing, 2016.
4.  C. Li, W. Chen, and Y. Tan, “Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation”, Appl. Sci. 10(18), 6439 (2020), doi: 10.3390/app10186439.
5.  Z. Fatemeh, S. Nicola, K. Satheesh, and U. Eranga, “Ensemble U‐net‐based method for fully automated detection and segmentation of renal masses on computed tomography images”, Med. Phys. 47(9), 4032‒4044 (2020), doi: 10.1002/mp.14193.
6.  O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, ArXiv, abs/1505.04597, 2015.
7.  M.E.J. Ferlay, F. Lam, M. Colombet, L. and Mery. “Global Cancer Observatory: Cancer Today.” [Online] Available: https://gco.iarc.fr/ today, accessed (accessed).
8.  P.A. Humphrey, H. Moch, A.L. Cubilla, T. M. Ulbright, and V.E. Reuter, “The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part B: Prostate and Bladder Tumours”, Eur. Urol. 70(1), 106‒119 (2016), doi: 10.1016/j.eururo.2016.02.028.
9.  D.L. Pham, C. Xu, and J.L. Prince, “Current Methods in Medical Image Segmentation”, Ann. Rev. Biomed. Eng. 2(1), 315‒337 (2000), doi: 10.1146/annurev.bioeng.2.1.315.
10.  B. Tsagaan, A. Shimizu, H. Kobatake, and K. Miyakawa, “An Automated Segmentation Method of Kidney Using Statistical Information”, in Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, pp. 556‒563, Springer Berlin Heidelberg, 2002.
11.  J.C. Bezdek, “Objective Function Clustering”, in Pattern Recognition with Fuzzy Objective Function Algorithms , pp. 43‒93, Boston: Springer US, 1981.
12.  K. Sharma et al., “Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease”, Sci. Rep. 7(1), 2049 (2017), doi: 10.1038/s41598-017-01779-0.
13.  P. Jackson, N. Hardcastle, N. Dawe, T. Kron, M.S. Hofman, and R. J. Hicks, “Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy”, Front. Oncol. 14(8), 215, (2018), doi: 10.3389/fonc.2018.00215.
14.  C. Li, W. Chen, and Y. Tan, “Point-Sampling Method Based on 3D U-Net Architecture to Reduce the Influence of False Positive and Solve Boundary Blur Problem in 3D CT Image Segmentation”, Appl. Sci. 10(19), 6838 (2020).
15.  A. Myronenko and A. Hatamizadeh, “3d kidneys and kidney tumor semantic segmentation using boundary-aware networks”, arXiv preprint arXiv:1909.06684, 2019.
16.  W. Zhao, D. Jiang, J. P. Queralta, and T. Westerlund, “Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation”, arXiv preprint arXiv:2004.08108, 2020.
17.  W. Zhao, D. Jiang, J. Peña Queralta, and T. Westerlund, “MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net”, Inform. Med. Unlocked 19, 100357 (2020), doi: 10.1016/j.imu.2020.100357.
18.  Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series”, in The handbook of brain theory and neural networks, pp. 255–258, MIT Press, 1998.
19.  T. Les, T. Markiewicz, M. Dziekiewicz, and M. Lorent, “Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis”, Appl. Sci. 10(21), 7512 (2020), doi: 10.3390/app10217512.
20.  Z. Swiderska-Chadaj, T. Markiewicz, J. Gallego, G. Bueno, B. Grala, and M. Lorent, “Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 849‒856 (2018).
21.  W. Wieclawek, “3D marker-controlled watershed for kidney segmentation in clinical CT exams”, Biomed. Eng. Online 17(1), 26 (2018), doi: 10.1186/s12938-018-0456-x.
22.  T. Les, “Patch-based renal CTA image segmentation with U-Net”, in 2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE), Poland, 2020, pp. 1‒4, doi: 10.1109/CPEE50798.2020.9238735.

Date

12.04.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137051

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e137051