title: Ð¡ÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð¹ ÑÐ¾Ð¼Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ Ð»ÑÐ³ÐºÐ¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Segmentation of images of computed tomography of the lungs using a convolutional neural network
Creators: name: ÐÑÐ¸Ð¼Ð¾Ð²Ð°, ÐÐ½Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
ÐºÑ-Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
yolact
Ð¾Ð±ÑÐµÐºÑ
ÑÐ²ÐµÑÑÐºÐ°
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¿Ð¾Ð´Ñ Ð¾Ð´Ñ
segmentation
ct images
computer vision
object
convolution
algorithm
convolutional neural network
classical approaches

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð½Ð°Ð¸Ð»ÑÑÑÐµÐ³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² Ð¿Ð¾Ð²ÑÐµÐ´Ð½ÐµÐ²Ð½Ð¾Ð¹ Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ Ð² Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ ÑÑÑÐµÐ¶Ð´ÐµÐ½Ð¸ÑÑ Ð²ÑÐ°ÑÐ°Ð¼Ð¸ Ð¸ Ð±ÑÑÑÑÐ¾Ð³Ð¾ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ð¸ Ð°Ð½Ð¾Ð¼Ð°Ð»ÑÐ½ÑÑ ÑÑÑÑÐºÑÑÑ Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ð¸ Ð±Ð¾Ð»ÐµÐ·Ð½Ð¸. ÐÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð²: Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð¾ Ð¿Ð¾ÑÐ¾Ð³Ñ, Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÐºÑÐ°ÐµÐ², Ð¼ÐµÑÐ¾Ð´ Ð½Ð°ÑÐ°ÑÐ¸Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹, FCN, SegNet, YOLACT, DeepLabv3, U-Net, V-Net, 3D-CNN, FC-DenseNet. ÐÑÑÐ¾Ð´Ñ Ð¸Ð· Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÑÐ²Ð»ÐµÐ½ Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ð¹ Ð¼ÐµÑÐ¾Ð´ Ð´Ð»Ñ ÑÐ°Ð±Ð¾ÑÑ Ñ ÐÐ¢-ÑÐ½Ð¸Ð¼ÐºÐ°Ð¼Ð¸ Ð»ÐµÐ³ÐºÐ¸Ñ: YOLACT, ÐºÐ¾ÑÐ¾ÑÑÐ¹ ÑÐ²Ð»ÑÐµÑÑÑ Ð±Ð¾Ð»ÐµÐµ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»ÑÑ Ð±Ð»Ð°Ð³Ð¾Ð´Ð°ÑÑ ÐºÐ¾Ð¼Ð¿ÑÐ¾Ð¼Ð¸ÑÑÐ°Ð¼ Ð² ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð½Ð¾Ð¼ Ð²ÑÑÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ, ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ñ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð¸ Ð² ÑÐ°Ð±Ð»Ð¸ÑÐ°Ñ. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÐ»Ð°ÑÑ Ð½Ð° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¸ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ OpenCV.
description: This work is devoted to the implementation of the best segmentation algorithm for use in everyday practice in medical institutions by doctors and quick decision-making in detecting abnormal structures and determining the disease. An overview of modern architectures based on convolutional neural networks and classical approaches is made threshold binarization, edge detection, area augmen-tation method, FCN, SegNet, YOLACT, DeepLabv3, U-Net, V-Net, 3D-CNN, FC-DenseNet. Based on this analysis, the best method for working with CT scans of the lungs was identified: YOLACT, which is a more optimal automated model due to trade-offs in speed and accuracy. The selected algorithm was tested on a hand-created dataset, the results of the study are shown in the images and in tables. The implemen-tation was carried out in the Python programming language and using the OpenCV computer vision library.