title: ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð² ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ñ Chan-Vese
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: Building a machine learning model for medical image segmentation in combination with Chan-Vese
Creators: name: Ð¤Ð°Ð¹Ð·ÑÐ°ÑÐ¼Ð°Ð½Ð¾Ð², ÐÑÑÑÑ
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: 2023
Subjects: ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Chan-Vese
Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
U-Net Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÐ°
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
segmentation
Chan-Vese algorithm
medical images
U-Net architecture
neural networks

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Chan-Vese Ð¸ Unet ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ°Ð»Ð¸ÑÑ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸: Â  Â  1) Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Unet Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸, Â  Â  2) Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Chan-Vese Ð´Ð»Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¼Ð°ÑÐºÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ, Â  Â  3) ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð²ÐµÐ± ÑÐµÑÐ²Ð¸ÑÐ° Ð´Ð»Ñ Ð¾ÑÐ¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, Â  Â  4) ÑÐ¸ÑÐ»ÐµÐ½Ð½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ°Ð±Ð¾ÑÑ Ð½Ð° Ð¾ÑÐºÑÑÑÑÑ Ð´Ð°ÑÐ°ÑÐµÑÐ°Ñ. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Unet Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Chan-Vese. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÑÐ·ÐºÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ U-Net Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Chan-Vese ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Ð´Ð»Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ 2D Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÐ°Ð½Ð½ÑÐ¹ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Ð±ÑÐ» Ð²Ð½ÐµÐ´ÑÐµÐ½ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð²ÐµÐ± ÑÐµÑÐ²Ð¸Ñ. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° ÑÐ°Ð·Ð¼ÐµÑÐºÐ° Ð±Ð¾Ð»ÐµÐµ 5000 Ð´Ð²ÑÐ¼ÐµÑÐ½ÑÑ KT ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² Ð»ÐµÐ³ÐºÐ¸Ñ Ð¸Ð· Ð¾ÑÐºÑÑÑÐ¾Ð³Ð¾ Ð´Ð°ÑÐ°ÑÐµÑÐ°. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð¼ÐµÑÑÐ¸ÐºÐ¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð°ÑÐºÐ¸ Ð¸ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ°Ð±Ð¾ÑÑ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ°. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±Ð°ÑÐ¶ÐµÐ½Ð¸Ð¹, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ð¹ Ð¿Ð¾Ð»ÑÑÐ¸ÑÑ Ð²ÑÑÐ¾ÐºÐ¾Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸. ÐÑÐ¸ ÑÑÐ¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð·Ð°Ð½Ð¸Ð¼Ð°ÐµÑ Ð¼Ð°Ð»Ð¾ ÑÐµÑÑÑÑÐ¾Ð² ÐºÐ°Ðº Ð¿Ð¾ Ð¿Ð°Ð¼ÑÑÐ¸, ÑÐ°Ðº Ð¸ Ð¿Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸.
description: This work is devoted to the creation of an automatic medical image segmentation tool based on the Chan-Vese algorithm and Unet convolutional neural network. During the study, the following tasks were solved: Â  Â  1) study and implementation of Unet architecture for solving the segmentation problem, Â  Â  2) learning the Chan-Vese algorithm to improve the segmented image mask, Â  Â  3) implementation of a web service for displaying and segmenting images, Â  Â  4) numerical study of segmentation quality and operating time on open datasets. The paper presents the Unet model of convolutional neural networks and the Chan-Vese algorithm. Based on the combination of the U-Net model and the Chan-Vese algorithm, a tool for automatic segmentation of 2D images has been developed. This tool was implemented in the developed web service. More than 5,000 2D CT scans of the lungs from an open dataset were also labeled. The accuracy metrics of the obtained mask and the metrics of the tool operation and training time are presented. As a result, a tool for automatic segmentation of medical images was developed, which allows obtaining high quality segmentation. At the same time, training the model takes up little resources both in terms of memory and time.