Open AccessAutomatic highlighting of the region of interest in computed tomography images of the lungs
Author(s) -
T. A. Pashina,
Andrey Gaidel,
P. M. Zelter,
А. В. Капишников,
Артем Никоноров
Publication year - 2020
Publication title -
kompʹûternaâ optika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 29
eISSN - 2412-6179
pISSN - 0134-2452
DOI - 10.18287/2412-6179-co-659
Subject(s) - artificial intelligence , region of interest , convolutional neural network , computer science , segmentation , pattern recognition (psychology) , computer vision , artificial neural network , deep learning , computed tomography , radiology , medicine
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.