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Domain Adaptation for Medical Image Segmentation: A Meta-Learning Method
Article

Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

1
Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
2
Sector of Data Analysis for Neuroscience, Kharkevich Institute for Information Transmission Problems, 127051 Moscow, Russia
3
Department of Radio Engineering and Cybernetics, Moscow Institute of Physics and Technology, 141701 Moscow, Russia
4
Moscow Gamma-Knife Center, 125047 Moscow, Russia
5
Department of Radiosurgery and Radiation, Burdenko Neurosurgery Institute, 125047 Moscow, Russia
6
Medical Research Department, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies of the Department of Health Care of Moscow, 127051 Moscow, Russia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Yudong Zhang, Juan Manuel Gorriz and Zhengchao Dong
J. Imaging 2021, 7(2), 35; https://doi.org/10.3390/jimaging7020035
Received: 31 December 2020 / Revised: 28 January 2021 / Accepted: 5 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets. View Full-Text
Keywords: deep learning; medical image segmentation; computed tomography (CT); magnetic resonance imaging (MRI) deep learning; medical image segmentation; computed tomography (CT); magnetic resonance imaging (MRI)
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MDPI and ACS Style

Shirokikh, B.; Shevtsov, A.; Dalechina, A.; Krivov, E.; Kostjuchenko, V.; Golanov, A.; Gombolevskiy, V.; Morozov, S.; Belyaev, M. Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization. J. Imaging 2021, 7, 35. https://doi.org/10.3390/jimaging7020035

AMA Style

Shirokikh B, Shevtsov A, Dalechina A, Krivov E, Kostjuchenko V, Golanov A, Gombolevskiy V, Morozov S, Belyaev M. Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization. Journal of Imaging. 2021; 7(2):35. https://doi.org/10.3390/jimaging7020035

Chicago/Turabian Style

Shirokikh, Boris, Alexey Shevtsov, Alexandra Dalechina, Egor Krivov, Valery Kostjuchenko, Andrey Golanov, Victor Gombolevskiy, Sergey Morozov, and Mikhail Belyaev. 2021. "Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization" Journal of Imaging 7, no. 2: 35. https://doi.org/10.3390/jimaging7020035

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