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Automatic highlighting of the region of interest in computed tomography images of the lungs

T.A. Pashina 1, A.V. Gaidel 1,2, P.M. Zelter 3, A.V. Kapishnikov 3, A.V. Nikonorov 1,2

1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,

2 IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS,
Molodogvardeyskaya 151, 443001, Samara, Russia,

3 Samara State Medical University, Samara, Russia

 PDF, 560 kB

DOI: 10.18287/2412-6179-CO-659

Pages: 74-81.

Full text of article: Russian language.

Abstract:
This article discusses the creation of masks for highlighting the lungs in computed tomography images using three methods – the Otsu method, a simple convolutional neural network consisting of 10 identical layers, and the convolutional neural network U-Net. We perform a study and comparison of methods used for automatically highlighting the region of interest (ROI) in computed tomography images of the lungs, which were provided as a courtesy from the Clinics of Samara State Medical University. The solution to this problem is relevant, because medical workers have to manually select the ROI as the first step of the automated processing of lung CT images. An algorithm for post-processing images based on the search for contours, which allows one to improve the quality of segmentation, is proposed. It is concluded that the U-Net highlights the ROI relating to the lung better than the other two methods. At the same time, the simple convolutional neural network highlights the ROI with an accuracy of 97.5%, which is better than the accuracy of  96.7% of the Otsu method and 96.4% of the U-Net.

Keywords:
image processing, computed tomography of the lungs, convolutional neural networks, U-Net.

Citation:
Pashina TA, Gaidel AV, Zelter PM, Kapishnikov AV, Nikonorov AV. Automatic highlighting of the region of interest in computed tomography images of the lungs. Computer Optics 2020; 44(1): 74-81. DOI: 10.18287/2412-6179-CO-659.

Acknowledgements:
The work was partially funded by the Russian Foundation for Basic Research under grants No. 18-07-01390, 19-29-01235 and 19-29-01135 (theoretical results) and the RF Ministry of Science and Higher Education within the government project of the FSRC “Crystallography and Photonics” RAS under grant No. 007-GZ/Ch3363/26 (numerical calculations).

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