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Volume 34 #4

Contents

  1. Plasticity of inertial mass in the equilibrium organ within an altering gravitational field
  2. Behavioral and neurophysiological characteristics of oblique orientations estimation in males and females
  3. The role of slow sodium channels in GABA and NO-ergic modulation of the excitability of a nociceptive neuron
  4. Prolab: perceptually uniform projective colour coordinate system
  5. A convolutional neural network, robust to label noise for glioma segmentation
  6. Using window hough transform for detecting elongated boundaries in an image

A convolutional neural network, robust to label noise for glioma segmentation

© 2020 T. N. Saparov, A. I. Kurmukov, B. N. Shirokih, S. V. Zolotova, A. V. Golovanov, M. G. Belyaev, A. V. Dalechina

Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), 127994 Moscow, Bol’shoy Karetny Pereulok, 19, Russian
Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Moscow Region, Institutskiy Pereulok, 9, Russian
Higher School of Economics – National Research University, 101000 Moscow, Myasnitskaya Ulitsa, 20, Russian
Skolkovo Institute of Science and Technology, 143026 Moscow, Bol’shoy Bul’var, 30, Russian
Federal State Autonomous Institution “N.N. Burdenko National Medical Research Center of Neurosurgery” of the Ministry of Health of the Russian Federation, 125047 Moscow, 4-Ya Tverskaya-Yamskaya Ulitsa, 16, Russian
Moscow Gamma Knife Center, Joint Stock Company “Neurosurgery Business Center”, 125047 Moscow, 1-Y Tverskoy-Yamskoy Pereulok, 13/5, Russian

Received 05 Jun 2020

Medical image segmentation is one of the most important tasks in radiotherapy. Recently, deep neural networks have achieved great success in solving a wide variety of medical image segmentation tasks using standardized or/and hardly preprocessed data. However, with typical clinical data, the situation drastically changes because of data complexity. For example, in radiotherapy tasks, data are much more variable in sizes and pixel intensities depending on a particular MRI machine. This heterogeneity leads to high intra- and interrater variability in tumor delineation. Finally, tumor contours might not coincide with pathological regions on the MRI image due to complex delineation procedures. Thus, the annotations in the existing datasets are relatively noisy. We propose a robust learning procedure based on the modern Convolutional Neural Network architecture. Our extension uses a separate module with learnable parameters, which outputs weights for the weighted cross-entropy. In our experiments, we achieved an impressive 38% boost in the Dice score using the data with high variability and target noise.

Key words: medical image segmentation, MRI, deep learning, high grade glioma

DOI: 10.31857/S0235009220040071

Cite: Saparov T. N., Kurmukov A. I., Shirokih B. N., Zolotova S. V., Golovanov A. V., Belyaev M. G., Dalechina A. V. Ustoichivaya k shumu v razmetke svertochnaya neironnaya set v zadache segmentatsii gliom na mrt izobrazheniyakh [A convolutional neural network, robust to label noise for glioma segmentation]. Sensornye sistemy [Sensory systems]. 2020. V. 34(4). P. 329–339 (in Russian). doi: 10.31857/S0235009220040071

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