ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 6. Vol. 28. 2022

DOI: 10.17587/it.28.326-333

M. S. Zabotnev, Cand. of Sc., Assistant Professor, V. P. Kulagin, Dr. of Sc., Professor, V. D. Korepanov, Graduate Student, MIREA — Russian Technological University, 119454, Moscow, Russian Federation

Applying a Synthetic Dataset in Medical Images Segmentation Problem

The paper is devoted to the problem of vessel segmentation in CT scans of human lungs. Segmentation is performed based on a machine learning model — a U-Net type convolutional neural network. To train the model, we need a set of segmented images — a training dataset. Manual segmentation of blood vessels on CT images is a laborious task. Also there are no open lungs vessels labeled or segmented datasets available at the moment. In the paper we propose an alternative approach — algorithmic generation of a vascular trees and further using of these trees as a synthetic dataset. We investigate the possibilities of training the model on such dataset and its further use for segmentation of real CT scans of the lungs.
Keywords: medical image segmentation, vascular segmentation, machine learning, synthetic dataset, lung segmentation, computed tomography, convolutional neural network, CNN, U-Net

P. 326–333

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