Tytuł artykułu

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

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences Technical Sciences

Rocznik

2021

Wolumin

69

Numer

3

Afiliacje

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

Autorzy

Les, Tomasz

Słowa kluczowe

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

Wydział PAN

Nauki Techniczne

Zakres

e137051

Bibliografia

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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).
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Data

12.04.2021

Typ

Article

Identyfikator

DOI: 10.24425/bpasts.2021.137051 ; ISSN 2300-1917

Źródło

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