Wu Deng , Xiaohai He , Jia Xu , Boyuan Ding , Songcen Dai , Chao Wei , Hui Pu , Yi Wei , Xinpeng Ren
{"title":"Optical MRI imaging based on computer vision for extracting and analyzing morphological features of renal tumors","authors":"Wu Deng , Xiaohai He , Jia Xu , Boyuan Ding , Songcen Dai , Chao Wei , Hui Pu , Yi Wei , Xinpeng Ren","doi":"10.1016/j.slast.2024.100192","DOIUrl":null,"url":null,"abstract":"<div><p>Computer vision technology is more and more widely used in the market. Target detection and feature extraction are two important auxiliary means of this technique, which are helpful to analyze target motion data. However, in the field of biology, there are some data limitations in the analysis of targets such as bacteria and tumors, which need to be further explored. Optical MRI imaging technology based on computer vision provides a new way to extract and analyze morphological features of renal tumors. In this paper, an optical MRI imaging method based on computer vision is designed and developed for the extraction and analysis of morphological features of kidney tumors. By using optical MRI imaging technology based on computer vision, the morphological characteristics of kidney tumors were extracted by analyzing the optical characteristics and MRI images of kidney tumors, and a simulation model was established to simulate the morphological characteristics of different types of kidney tumors, and feature extraction and analysis were carried out by computer algorithm. Through the analysis of the simulation model, the morphological characteristics of renal tumors were extracted and analyzed, which provided a new and non-invasive method for clinical diagnosis and treatment of renal tumors.</p></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 5","pages":"Article 100192"},"PeriodicalIF":2.5000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2472630324000748/pdfft?md5=a2a8b1de98cd33816bde3cd2c8ae7b4f&pid=1-s2.0-S2472630324000748-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SLAS Technology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2472630324000748","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Computer vision technology is more and more widely used in the market. Target detection and feature extraction are two important auxiliary means of this technique, which are helpful to analyze target motion data. However, in the field of biology, there are some data limitations in the analysis of targets such as bacteria and tumors, which need to be further explored. Optical MRI imaging technology based on computer vision provides a new way to extract and analyze morphological features of renal tumors. In this paper, an optical MRI imaging method based on computer vision is designed and developed for the extraction and analysis of morphological features of kidney tumors. By using optical MRI imaging technology based on computer vision, the morphological characteristics of kidney tumors were extracted by analyzing the optical characteristics and MRI images of kidney tumors, and a simulation model was established to simulate the morphological characteristics of different types of kidney tumors, and feature extraction and analysis were carried out by computer algorithm. Through the analysis of the simulation model, the morphological characteristics of renal tumors were extracted and analyzed, which provided a new and non-invasive method for clinical diagnosis and treatment of renal tumors.
期刊介绍:
SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.